phd medical statistics

DPhil in Clinical Epidemiology and Medical Statistics

• Entry Requirements
• Funding and Costs

College preference

• How to Apply

About the course

As a DPhil student in Clinical Epidemiology and Medical Statistics, you will spend up to four years in one of the Botnar Research Centre’s many research groups, working on a research project supervised by one of the principal investigators and your supervisory team. You will take part in the extensive training programme specifically organised for graduate students within the department.

This DPhil programme focuses on epidemiology, medical statistics, clinical trials, real world health data, research methodology, artificial intelligence and machine learning, and health economics - aiming to advance healthcare practice and policy to ultimately generate reliable evidence for improving patient care.

You will develop your research skills during your first year, including compulsory attendance at the department's fundamentals in biomedical research lectures. During the first term you will develop, in consultation with your supervisor, a training needs plan. Your training will be tailored to your specific project and personal requirements drawing from the vast range of courses available at Oxford and covering specialist scientific methods and transferable skills. Please note that there is no formal taught component of the DPhil in Clinical Epidemiology and Medical Statistics; however, you will develop your research skills through a range of research training in your first year and by attending departmental/institute journal clubs and seminar series.

During the first term there is compulsory attendance at core lectures on a variety of research techniques and research areas covered in the department including:

• inflammation
• tissue engineering
• clinical trial design
• epidemiology
• rheumatology
• orthopaedics
• musculoskeletal diseases.

During your first year, you will be expected to attend a minimum of three topic-related modules.

As a member of Medical Sciences Graduate School, you will be entitled to attend various workshops run by the Medical Sciences Skills Training  programme which are run during term time.

Attendance on a two-day Data Analysis: Statistics Designing Clinical Research and Biostatistics course is compulsory (if you have had no previous statistical training) to assist you with appropriate research design. As a component of your training, you will be expected to work with your supervisory team to write a research-specific literature review within the first year of your studies.

Supervision

The allocation of graduate supervision for this course is the responsibility of Medical Sciences and the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS) and it is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff. Under exceptional circumstances a supervisor may come from other departments in the University.

The allocation of graduate supervision for this course is the responsibility of Medical Sciences and the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS) and it is not always possible to accommodate the preferences of incoming graduate students to work with a particular member of staff. Under exceptional circumstances a supervisor may be found outside Medical Sciences and NDORMS.

All students must have a core supervisory team of at least two supervisors. At the beginning of your programme, you will meet with your supervisors regularly to finalise your project and agree on an initial programme of research. Within the first three months, you will complete an analysis of your training needs (TNA) for the duration of your research, with your primary supervisor, and submit it as part of your compulsory termly reporting through the Graduate Supervision Reporting system (GSR).

Regularity of student/supervisor meetings will be agreed between the student and supervisors directly. Every student should meet their supervisors at least once per month. The Thesis Committee is an important second strand of supervisory support and is compulsory at the Botnar Research Centre; further information can be provided by the Graduate Studies Team.

Within the first six to twelve months you are expected to complete a literature review on your DPhil research which will assist you in have a broad knowledge on the background of the subject.

In the case of students who require specific help to adjust to an academic programme or to a new range of skills, the supervisor will work with them to ensure that they have the necessary additional support.

Your attainment will be monitored regularly via:

• Completion of termly reports by you and your supervisor(s) through Graduate Supervision Reporting (GSR)
• Successful completion of the first milestone, Transfer of Status- before the end of the fourth term. The process includes preparation and submission of a 5000-word transfer report and assessment by two independent academics, in a viva voce.
• Successful completion of the second milestone, Confirmation of Status- before the end of the eighth term. This process includes assessment by two independent academics, in a viva voce. The assessment includes the student providing a detailed presentation of their findings, an outline of the student's thesis and a viva.
• Successful completion of the final milestone, submission and defence of the DPhil thesis, no later than twelfth term. The student's thesis will be formally examined by independent internal (to Oxford University) and external examiners, who will scrutinise the student's findings and the depth/breadth of their knowledge on their DPhil research.

Stages 2, 3 and 4 will be assessed by two independent senior academics to ensure you are on track with your research and that you are receiving adequate guidance.

Graduate destinations

According to the department's records, NDORMS alumni are employed, across a wide range of clinical professions (eg rheumatology, orthopaedics or physiotherapy) and non-clinical related professions (eg in postdoctoral academic and industrial research, teaching, pharmaceuticals, marketing and scientific writing). A number of alumni have set up their own businesses or changed paths completely, into banking or medical writing.

The Director of Graduate Studies and Graduate Studies Assistant follow the department's alumni to establish the long-term career paths of past students.

Changes to this course and your supervision

The University will seek to deliver this course in accordance with the description set out in this course page. However, there may be situations in which it is desirable or necessary for the University to make changes in course provision, either before or after registration. The safety of students, staff and visitors is paramount and major changes to delivery or services may have to be made in circumstances of a pandemic, epidemic or local health emergency. In addition, in certain circumstances, for example due to visa difficulties or because the health needs of students cannot be met, it may be necessary to make adjustments to course requirements for international study.

Where possible your academic supervisor will not change for the duration of your course. However, it may be necessary to assign a new academic supervisor during the course of study or before registration for reasons which might include illness, sabbatical leave, parental leave or change in employment.

For further information please see our page on changes to courses and the provisions of the student contract regarding changes to courses.

Entry requirements for entry in 2024-25

Proven and potential academic excellence.

The requirements described below are specific to this course and apply only in the year of entry that is shown. You can use our interactive tool to help you  evaluate whether your application is likely to be competitive .

Please be aware that any studentships that are linked to this course may have different or additional requirements and you should read any studentship information carefully before applying. 

Degree-level qualifications

As a minimum, applicants should hold or be predicted to achieve the following UK qualifications or their equivalent:

• a first-class or strong upper second-class undergraduate degree with honours as a minimum, in statistics, epidemiology, health economics and/or related topics.

The department also considers applicants from medically qualified individuals. In special circumstances, applications from other medically related subjects (eg nurses, and/or allied health professionals) will be considered for the DPhil. If you fall into this category, please contact the Graduate Studies Officer .

You do not need to have a previous master's degree to be considered for this DPhil.

For applicants from the USA or China, the minimum GPA sought is 3.5 out of 4.0.

If your degree is not from the UK or another country specified above, visit our International Qualifications page for guidance on the qualifications and grades that would usually be considered to meet the University’s minimum entry requirements.

GRE General Test scores

No Graduate Record Examination (GRE) or GMAT scores are sought.

Other qualifications, evidence of excellence and relevant experience

• Research or working experience in any field may be an advantage. For clinical applicants, evidence of your employer's support will be required.
• In exceptional circumstances, an applicant could be considered if they have substantial professional experience in a statistical/epidemiological-related field.
• It would be expected that graduate applicants would be familiar with the recent published work of their proposed supervisor.
• Although it is not essential, preference will be given to applicants who have recent publications and/or awards from various funding bodies.

English language proficiency

This course requires proficiency in English at the University's  standard level . If your first language is not English, you may need to provide evidence that you meet this requirement. The minimum scores required to meet the University's standard level are detailed in the table below.

*Previously known as the Cambridge Certificate of Advanced English or Cambridge English: Advanced (CAE) † Previously known as the Cambridge Certificate of Proficiency in English or Cambridge English: Proficiency (CPE)

Your test must have been taken no more than two years before the start date of your course. Our Application Guide provides further information about the English language test requirement .

Declaring extenuating circumstances

If your ability to meet the entry requirements has been affected by the COVID-19 pandemic (eg you were awarded an unclassified/ungraded degree) or any other exceptional personal circumstance (eg other illness or bereavement), please refer to the guidance on extenuating circumstances in the Application Guide for information about how to declare this so that your application can be considered appropriately.

You will need to register three referees who can give an informed view of your academic ability and suitability for the course. The  How to apply  section of this page provides details of the types of reference that are required in support of your application for this course and how these will be assessed.

Supporting documents

You will be required to supply supporting documents with your application. The  How to apply  section of this page provides details of the supporting documents that are required as part of your application for this course and how these will be assessed.

Performance at interview

Interviews are normally held as part of the admissions process.

All shortlisted candidates will be interviewed in person or by video-conference. The interview will be conducted by up to six senior academics and it will last a maximum of 45 minutes. Those shortlisted for interviews will be notified 7 to 14 days prior to the interview date. 

The shortlisted applicants will be required to give a 10 minute presentation on their previous research or that proposed to be undertaken for the DPhil.

How your application is assessed

Your application will be assessed purely on your proven and potential academic excellence and other entry requirements described under that heading.

References  and  supporting documents  submitted as part of your application, and your performance at interview (if interviews are held) will be considered as part of the assessment process. Whether or not you have secured funding will not be taken into consideration when your application is assessed.

An overview of the shortlisting and selection process is provided below. Our ' After you apply ' pages provide  more information about how applications are assessed . 

Shortlisting and selection

Students are considered for shortlisting and selected for admission without regard to age, disability, gender reassignment, marital or civil partnership status, pregnancy and maternity, race (including colour, nationality and ethnic or national origins), religion or belief (including lack of belief), sex, sexual orientation, as well as other relevant circumstances including parental or caring responsibilities or social background. However, please note the following:

• socio-economic information may be taken into account in the selection of applicants and award of scholarships for courses that are part of  the University’s pilot selection procedure  and for  scholarships aimed at under-represented groups ;
• country of ordinary residence may be taken into account in the awarding of certain scholarships; and
• protected characteristics may be taken into account during shortlisting for interview or the award of scholarships where the University has approved a positive action case under the Equality Act 2010.

Initiatives to improve access to graduate study

This course is taking part in a continuing pilot programme to improve the selection procedure for graduate applications, in order to ensure that all candidates are evaluated fairly.

For this course, socio-economic data (where it has been provided in the application form) will be used to contextualise applications at the different stages of the selection process.  Further information about how we use your socio-economic data  can be found in our page about initiatives to improve access to graduate study.

Processing your data for shortlisting and selection

Information about  processing special category data for the purposes of positive action  and  using your data to assess your eligibility for funding , can be found in our Postgraduate Applicant Privacy Policy.

Admissions panels and assessors

All recommendations to admit a student involve the judgement of at least two members of the academic staff with relevant experience and expertise, and must also be approved by the Director of Graduate Studies or Admissions Committee (or equivalent within the department).

Admissions panels or committees will always include at least one member of academic staff who has undertaken appropriate training.

Other factors governing whether places can be offered

The following factors will also govern whether candidates can be offered places:

• the ability of the University to provide the appropriate supervision for your studies, as outlined under the 'Supervision' heading in the  About  section of this page;
• the ability of the University to provide appropriate support for your studies (eg through the provision of facilities, resources, teaching and/or research opportunities); and
• minimum and maximum limits to the numbers of students who may be admitted to the University's taught and research programmes.

Offer conditions for successful applications

If you receive an offer of a place at Oxford, your offer will outline any conditions that you need to satisfy and any actions you need to take, together with any associated deadlines. These may include academic conditions, such as achieving a specific final grade in your current degree course. These conditions will usually depend on your individual academic circumstances and may vary between applicants. Our ' After you apply ' pages provide more information about offers and conditions . 

In addition to any academic conditions which are set, you will also be required to meet the following requirements:

Financial Declaration

If you are offered a place, you will be required to complete a  Financial Declaration  in order to meet your financial condition of admission.

Disclosure of criminal convictions

In accordance with the University’s obligations towards students and staff, we will ask you to declare any  relevant, unspent criminal convictions  before you can take up a place at Oxford.

Academic Technology Approval Scheme (ATAS)

Some postgraduate research students in science, engineering and technology subjects will need an Academic Technology Approval Scheme (ATAS) certificate prior to applying for a  Student visa (under the Student Route) . For some courses, the requirement to apply for an ATAS certificate may depend on your research area.

The Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS) is a large multi-disciplinary department with a broad range of sciences related to medicine. Research spans the translational research spectrum, from basic biological research through to clinical and epidemiological research. 

The NDORMS is committed to training the next generation of scientists in biological and clinical sciences and has a large number of staff (over 400 people), approximately 100 postgraduate research students and a grant portfolio in excess of £150 million.

NDORMS has state-of-the-art research facilities across the spectrum of our research expertise.

There is student representation within the various departmental committees, providing student-led support as well as representing students’ interests in departmental decision-making.

You will have access to a wide range of resources within the department and University, including the following facilities.

You will have access to University IT services and Medical Sciences Division IT support. You will be allocated unique single-sign-on (SSO) credentials which will allow you to access numerous resources such as information on local seminars (Oxford Talk), other departmental and University information, the divisional skills training portal, Researchers' Toolkit, significant information on the University's student gateway, career courses and libraries online.

You will have access to local libraries: the Bodleian Library, the Cairns Library based in the John Radcliffe Hospital and musculoskeletal-related topics at the Girdlestone Library located at the Nuffield Orthopaedic Centre's Knowledge Centre on the Old Road Campus. Furthermore, through the central University library services, you will have access to a wide range of articles and publications.

Study and experimental space

You will be allocated an office space/working station that may be shared undertaking data analyses and computer-based research.

Lectures and seminars

You will be notified by regular emails about seminar schedules within the department and you are encouraged to visit the Oxford Talk website to access other departments' and divisions' seminars and lectures.

NDORMS Student Committee

Currently there are approximately 100 DPhil and MSc research students. There is an active student committee which organises regular social events, a Christmas gathering with a band, and a picnic in the park during the summer. At least two students are represented at the department’s Graduate Studies Committee, the Athena SWAN Committee and the University's Graduate Joint Consultative Committee to express students' opinions, concerns and views.

Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences

The Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS) is a vibrant multi-disciplinary department focusing on musculoskeletal and immunological diseases, from bench to bedside.

NDORMS is the largest European academic department in its field and runs a globally competitive programme of research and teaching, supported by a grants portfolio worth £169 million.

The department, headed by Professor Jonathan Rees, comprises over 400 staff including 45 professors/associate professors, approximately 100 graduate students and, several university lecturers and senior researchers supported by prestigious awards.

NDORMS has two institutes, the Botnar Research Centre (led by Professor Jonathan Rees) on the Nuffield Orthopaedic Centre (NOC) site, and the Kennedy Institute of Rheumatology (led by Professor Fiona Powrie) on the Old Road Campus. It also has a number of world-renowned units, including the Centre for Statistics in Medicine (led by Professor Gary Collins), the Oxford Clinical Trials Research Unit and the Kadoorie Centre for Critical Care Research (led by Professor Matt Costa) and Education (based in the John Radcliffe Hospital).

The Botnar Research Institute provides a unique setting for basic science researchers, statisticians and clinical trials experts to interact with clinician scientists, and to translate new experimental medicines and surgical designs into successful treatments. The Botnar Research Centre is strongly connected to the internationally renowned NOC, providing crucial access to patients' samples and an overall capacity for clinical and surgical trials.

The Kennedy Institute carries out basic and clinical research in chronic inflammatory and degenerative diseases including arthritis and inflammatory bowel disease. The Kennedy Institute is famous for its development of anti-TNF therapy to treat rheumatoid arthritis, a chronic debilitating disease. This treatment has improved the lives of millions of patients around the world.

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The University expects to be able to offer over 1,000 full or partial graduate scholarships across the collegiate University in 2024-25. You will be automatically considered for the majority of Oxford scholarships , if you fulfil the eligibility criteria and submit your graduate application by the relevant December or January deadline. Most scholarships are awarded on the basis of academic merit and/or potential. 

For further details about searching for funding as a graduate student visit our dedicated Funding pages, which contain information about how to apply for Oxford scholarships requiring an additional application, details of external funding, loan schemes and other funding sources.

Please ensure that you visit individual college websites for details of any college-specific funding opportunities using the links provided on our college pages or below:

Please note that not all the colleges listed above may accept students on this course. For details of those which do, please refer to the College preference section of this page.

Further information about funding opportunities for this course can be found on the department's website.

Annual fees for entry in 2024-25

Further details about fee status eligibility can be found on the fee status webpage.

Information about course fees

Course fees are payable each year, for the duration of your fee liability (your fee liability is the length of time for which you are required to pay course fees). For courses lasting longer than one year, please be aware that fees will usually increase annually. For details, please see our guidance on changes to fees and charges .

Course fees cover your teaching as well as other academic services and facilities provided to support your studies. Unless specified in the additional information section below, course fees do not cover your accommodation, residential costs or other living costs. They also don’t cover any additional costs and charges that are outlined in the additional information below.

Continuation charges

Following the period of fee liability , you may also be required to pay a University continuation charge and a college continuation charge. The University and college continuation charges are shown on the Continuation charges page.

Where can I find further information about fees?

The Fees and Funding  section of this website provides further information about course fees , including information about fee status and eligibility  and your length of fee liability .

Additional information

There are no compulsory elements of this course that entail additional costs beyond fees (or, after fee liability ends, continuation charges) and living costs. However, please note that, depending on your choice of research topic and the research required to complete it, you may incur additional expenses, such as travel expenses, research expenses, and field trips. You will need to meet these additional costs, although you may be able to apply for small grants from your department and/or college to help you cover some of these expenses.

Living costs

In addition to your course fees, you will need to ensure that you have adequate funds to support your living costs for the duration of your course.

For the 2024-25 academic year, the range of likely living costs for full-time study is between c. £1,345 and £1,955 for each month spent in Oxford. Full information, including a breakdown of likely living costs in Oxford for items such as food, accommodation and study costs, is available on our living costs page. The current economic climate and high national rate of inflation make it very hard to estimate potential changes to the cost of living over the next few years. When planning your finances for any future years of study in Oxford beyond 2024-25, it is suggested that you allow for potential increases in living expenses of around 5% each year – although this rate may vary depending on the national economic situation. UK inflationary increases will be kept under review and this page updated.

Students enrolled on this course will belong to both a department/faculty and a college. Please note that ‘college’ and ‘colleges’ refers to all 43 of the University’s colleges, including those designated as societies and permanent private halls (PPHs). 

If you apply for a place on this course you will have the option to express a preference for one of the colleges listed below, or you can ask us to find a college for you. Before deciding, we suggest that you read our brief  introduction to the college system at Oxford  and our  advice about expressing a college preference . For some courses, the department may have provided some additional advice below to help you decide.

The following colleges accept students for the DPhil in Clinical Epidemiology and Medical Statistics:

• Green Templeton College
• Lady Margaret Hall
• Linacre College
• St Anne's College
• St Catherine's College
• St Hilda's College
• Wolfson College

Before you apply

We strongly recommend you consult the Medical Sciences Graduate School's research themes to identify the most suitable course and supervisor .

Our  guide to getting started  provides general advice on how to prepare for and start your application.  You can use our interactive tool to help you evaluate whether your application is likely to be competitive .

If it's important for you to have your application considered under a particular deadline – eg under a December or January deadline in order to be considered for Oxford scholarships – we recommend that you aim to complete and submit your application at least two weeks in advance . Check the deadlines on this page and the  information about deadlines and when to apply  in our Application Guide.

Application fee waivers

An application fee of £75 is payable per course application. Application fee waivers are available for the following applicants who meet the eligibility criteria:

• applicants from low-income countries;
• refugees and displaced persons; 
• UK applicants from low-income backgrounds; and 
• applicants who applied for our Graduate Access Programmes in the past two years and met the eligibility criteria.

You are encouraged to  check whether you're eligible for an application fee waiver  before you apply.

Readmission for current Oxford graduate taught students

If you're currently studying for an Oxford graduate taught course and apply to this course with no break in your studies, you may be eligible to apply to this course as a readmission applicant. The application fee will be waived for an eligible application of this type. Check whether you're eligible to apply for readmission .

Application fee waivers for eligible associated courses

If you apply to this course and up to two eligible associated courses from our predefined list during the same cycle, you can request an application fee waiver so that you only need to pay one application fee.

The list of eligible associated courses may be updated as new courses are opened. Please check the list regularly, especially if you are applying to a course that has recently opened to accept applications.

Do I need to contact anyone before I apply?

Before you apply (if you are not applying for an advertised project), you should approach a supervisor to ensure they have the capacity to take you on and are willing to support your application. You will also need to agree on a research project, a proposal for which should be submitted as part of your application. Details of potential supervisors  can be found on the department's website.

Completing your application

You should refer to the information below when completing the application form, paying attention to the specific requirements for the supporting documents .

For this course, the application form will include questions that collect information that would usually be included in a CV/résumé. You should not upload a separate document. If a separate CV/résumé is uploaded, it will be removed from your application .

If any document does not meet the specification, including the stipulated word count, your application may be considered incomplete and not assessed by the academic department. Expand each section to show further details.

Proposed field and title of research project

You must enter the project you are applying to under 'Field and title of research project' on the 'Course' tab of the application form.

You should not use this field to type out a full research proposal. You will be able to upload your research supporting materials separately if they are required (as described below).

Proposed supervisor

Under 'Proposed supervisor name' enter the name of the academic(s) who you would like to supervise your research. 

Referees Three overall, of which at least two must be academic

Whilst you must register three referees, the department may start the assessment of your application if two of the three references are submitted by the course deadline and your application is otherwise complete. Please note that you may still be required to ensure your third referee supplies a reference for consideration.

One professional reference is acceptable, though your other references should be academic and should comment specifically on your academic ability.

Your references should support your intellectual ability, academic achievement, motivation and ability to work independently.

Official transcript(s)

Your transcripts should give detailed information of the individual grades received in your university-level qualifications to date. You should only upload official documents issued by your institution and any transcript not in English should be accompanied by a certified translation.

More information about the transcript requirement is available in the Application Guide.

Statement of purpose/personal statement and research proposal: Up to 500 words for the personal statement and up to 2,000 words for the research proposal

All applicants should submit a personal statement. If you are not applying for specified studentships, you will also need to submit a research proposal.

Your statement of purpose/personal statement and research proposal should be submitted as a single, combined document with clear subheadings. Please ensure that the word counts for each section are clearly visible in the document.

Statement of purpose/personal statement

You should provide a statement of your research interests, in English, describing how your background and research interests relate to the programme. If possible, please ensure that the word count is clearly displayed on the document.

The statement should focus on academic or research-related achievements and interests rather than personal achievements and interests.

This will be assessed for:

• your reasons for applying;
• evidence of motivation for and understanding of the proposed area of study;
• the ability to present a reasoned case in English;
• capacity for sustained and focused work; and
• understanding of problems in the area and ability to construct and defend an argument.

It will be normal for students’ ideas and goals to change in some ways as they undertake their studies, but your personal statement will enable you to demonstrate your current interests and aspirations.

Research proposal

A research proposal should only be submitted if you are not applying for a specified studentship.

Your research proposal, should comprise a detailed outline of your proposed research, written in English. The research proposal should include details of the background/rationale of the research, hypotheses and methodology. It should explain the originality/novelty of the work and outline how completion within twelve academic terms (ie four years) can be achieved. The overall word count, of no more than 2,000 words, should include any bibliography.

If possible, please ensure that the word count is clearly displayed on the document.

Start or continue your application

You can start or return to an application using the relevant link below. As you complete the form, please  refer to the requirements above  and  consult our Application Guide for advice . You'll find the answers to most common queries in our FAQs.

Application Guide   Apply

ADMISSION STATUS

Closed to applications for entry in 2024-25

Register to be notified via email when the next application cycle opens (for entry in 2025-26)

12:00 midday UK time on:

Friday 1 December 2023 Latest deadline for most Oxford scholarships

A later deadline shown under 'Admission status' If places are still available,  applications may be accepted after 1 December . The 'Admissions status' (above) will provide notice of any later deadline.

*One year average (applications for entry in 2023-24)

Further information and enquiries

This course is offered by the  Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences

• Course page on the department's website
• Funding information from the department
• Academic and research staff
• Departmental research
• Medical Sciences Graduate School
• Residence requirements for full-time courses
• Postgraduate applicant privacy policy

Course-related enquiries

Advice about contacting the department can be found in the How to apply section of this page

✉ [email protected] ☎ +44 (0)1865 737641

Application-process enquiries

See the application guide

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About the PhD in Biostatistics Program

The PhD in Biostatistics provides training in the theory of probability and statistics in biostatistical methodology. The program is unique in its emphasis on the foundations of statistical reasoning and data science. Students complete rigorous training in real analysis-based probability and statistics, equivalent to what is provided in most departments of mathematical statistics and in advanced data science.

PhD candidates are required to pass a comprehensive written examination covering coursework completed at the end of their first year. Research leading to a thesis may involve development of new theory and methodology, or it may be concerned with applications of statistics and probability to problems in public health, medicine or biology.

Application Fee Waivers: We are able to offer a limited number of application fee waivers. Learn about the eligibility criteria and how to apply for a waiver .

PhD in Biostatistics Program Highlights

Conduct and publish original research.

on the theory and methodology of biostatistics

Apply innovative theory and methods

to the solution of public health problems

Serve as an expert biostatistician

on collaborative teams of investigators addressing key public health questions

Teach biostatistics effectively

to health professionals and scientists as well as to graduate students in biostatistics

What Can You Do With a PhD In Biostatistics?

Visit the Graduate Employment Outcomes Dashboard to learn about Bloomberg School graduates' employment status, sector, and salaries. We have over 750 global alumni working in academia, government, and industry.

Sample Careers and Next Steps

• Tenure Track Faculty (e.g. Assistant Professor)
• Postdoctoral Fellow
• Data Scientist
• Statistician
• Biostatistician
• Machine Learning Engineer
• Mathematical Statistician
• Principal Investigator

Curriculum for the PhD in Biostatistics

Browse an overview of the requirements for this PhD program in the JHU  Academic Catalogue  and explore all course offerings in the Bloomberg School  Course Directory .

Admissions Requirements

For general admissions requirements, please visit the How to Apply page. This specific program also requires:

Prior Coursework

Calculus and linear algebra; accepted applicants are also strongly encouraged to take real analysis before matriculating

Standardized Test Scores

Standardized test scores are  not required and not reviewed  for this program. If you have taken a standardized test such as the GRE, GMAT, or MCAT and want to submit your scores, please note that they will not be used as a metric during the application review.  Applications will be reviewed holistically based on all required application components.

Vivien Thomas Scholars Initiative

The  Vivien Thomas Scholars Initiative (VTSI)  is an endowed fellowship program at Johns Hopkins for PhD students in STEM fields. It provides full tuition, stipend, and benefits while also providing targeted mentoring, networking, community, and professional development opportunities. Students who have attended a historically Black college and university (HBCU) or other minority serving institution (MSI) for undergraduate study are eligible to apply. To be considered for the VTSI, you will need to submit a SOPHAS application ,VTSI supplementary materials, and all supporting documents (letters, transcripts, and test scores) by December 1, 2024. VTSI applicants are eligible for an  application fee waiver , but the fee waiver must be requested by November 15, 2024 and prior to submission of the SOPHAS application.

Per the Collective Bargaining Agreement (CBA) with the JHU PhD Union, the minimum guaranteed 2025-2026 academic year stipend is $50,000 for all PhD students with a 4% increase the following year. Tuition, fees, and medical benefits are provided, including health insurance premiums for PhD student’s children and spouses of international students, depending on visa type. The minimum stipend and tuition coverage is guaranteed for at least the first four years of a BSPH PhD program; specific amounts and the number of years supported, as well as work expectations related to that stipend will vary across departments and funding source. Please refer to the CBA to review specific benefits, compensation, and other terms.

Need-Based Relocation Grants

Students who  are admitted to PhD programs at JHU starting in Fall 2023 or beyond can apply to receive a need-based grant to offset the costs of relocating to be able to attend JHU.   These grants provide funding to a portion of incoming students who, without this money, may otherwise not be able to afford to relocate to JHU for their PhD program. This is not a merit-based grant. Applications will be evaluated solely based on financial need.  View more information about the need-based relocation grants for PhD students .

Questions about the program? We're happy to help. 

Academic Administrator Mary Joy Argo 410-614-4454 [email protected]

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Stanford Online

Medical statistics program.

Stanford School of Medicine , Stanford Center for Health Education

All Access Plan: $499 USD Per course $179 USD

Get Started

Medical statistics, a branch of biostatistics, is the science of collecting, summarizing, presenting, and interpreting data in relation to the medicine and health fields. Through its use in medical research and investigations, we can better understand health phenomena in our populations. By studying medical statistics, you will gain the statistical literacy needed to remain adept and adaptable in our ever-changing health industries. While the examples and applications are within the context of health and medicine, the statistical foundations you will gain can be applied to any industry.

• Analyze, interpret, describe, and visualize data
• Program in language R or SAS to graph your data
• Draw conclusions based on a sample or subset of data
• Apply the statistical methods you’ve learned to medical research
• Preview Image
• Course/Course #
• Time Commitment
• Availability

Medical Statistics I: Introduction to Data Analysis and Descriptive Statistics

Medical Statistics II: Probability and Inference

Medical Statistics III: Common Statistical Tests in Medical Research

Flexible enrollment options, enroll in individual courses.

Pay as you go

$179 per required course 60 days to complete

View and complete course materials, video lectures, assignments and exams, at your own pace. You also get 60 days of email access to your Stanford teaching assistant.

All-Access Plan

One Year Subscription

View and complete course materials, video lectures, assignments and exams, at your own pace. Revisit course materials or jump ahead – all content remains at your fingertips year-round. You also get 365 days of email access to your Stanford teaching assistant.

Groups and Teams

Special Pricing

Enroll as a group or team and learn together. We can advise you on the best group options to meet your organization’s training and development goals and provide you with the support needed to streamline the process. Participating together, your group will develop a shared knowledge, language, and mindset to tackle the challenges ahead.

What Our Learners Are Saying

The Medical Statistics Program from Stanford School of Medicine is pitched perfectly to enable medical professionals to critically interpret the statistical analyses in research literature that many of us accept unquestioningly. Dr Sainani's rare ability to explain and her enthusiasm for the subject are complemented with real-world examples of medical studies that illustrate fundamental principles and clarify underlying concepts. The mathematical content is kept to a non-intimidating minimum, but there are optional lessons that encourage one to explore further. I unreservedly recommend this course as being very educative, engrossing and, yes, enjoyable.

Sushil D., Professor of Surgery

Teaching Team

Kristin Sainani

Epidemiology and Population Health

Kristin Sainani (née Cobb) is an associate professor at Stanford University and also a health and science writer. After receiving an MS in statistics and a PhD in epidemiology from Stanford University, she studied science writing at the University of California, Santa Cruz. She has taught statistics and writing at Stanford for more than a decade and has received several Excellence in Teaching Awards from the graduate program in epidemiology. Dr. Sainani writes about science and health for a range of audiences. She authored the health column Body News for Allure magazine for a decade. She is also the statistical editor for the journal Physical Medicine & Rehabilitation; and she authors a statistics column, Statistically Speaking, for this journal.

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We have 52 medical statistics PhD Projects, Programmes & Scholarships

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medical statistics PhD Projects, Programmes & Scholarships

Comprehensive meta-analyses of surgical repair of groin hernia, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

PhD Studentship in Quantitative Magnetic Resonance Imaging MRI of the spine

Funded phd project (uk students only).

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

PHD MATHEMATICAL SCIENCES

Funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

China PhD Programme

A Chinese PhD usually takes 3-4 years and often involves following a formal teaching plan (set by your supervisor) as well as carrying out your own original research. Your PhD thesis will be publicly examined in front of a panel of expert. Some international programmes are offered in English, but others will be taught in Mandarin Chinese.

Leveraging Electronic Health Records: Bayesian Joint Longitudinal and Survival Modelling of Bivariate Longitudinal Trajectories for Two Disease Outcomes

Identifying mechanisms underlying the heterogeneity of cerebral small vessel disease, has covid-19 altered the relationship between residential geography and cancer experiences and outcomes in scotland, promoting healthier behaviours among black african and caribbean populations in the uk: a positive deviance approach (ref: ssehs/hok24), nanoengineering in pharmaceutical formulation, funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Fully funded (and no tuition) PhD program in psychiatric, translational research and basic Neuroscience with the option for a residency track for medical doctors.

Germany phd programme.

A German PhD usually takes 3-4 years. Traditional programmes focus on independent research, but more structured PhDs involve additional training units (worth 180-240 ECTS credits) as well as placement opportunities. Both options require you to produce a thesis and present it for examination. Many programmes are delivered in English.

The genetic map of human molecular phenotypes

Computational methods for medical image analysis: foundation models, generative models and multimodal learning, competition funded phd project (students worldwide).

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

PhD Studentship in the Health Economics Group: Investigating approaches to the estimation of lifetime progression-free and post-progression mortality rates in cancer patients

Joint modelling of latent trajectories for dynamic prediction of competing outcomes in patients with liver disease, ai powered personalized virtual heart modelling, determining the biological basis of observed clinical outcomes in juvenile idiopathic arthritis using genetics and multiomics.

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Learn more about our research centers, which focus on critical issues in public health.

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Learn how to apply to the Mailman School of Public Health. 

Doctoral Programs

Biostatistics.

Biostatistics applies statistical and probability theory to human health and disease. The PhD program in biostatistics prepares individuals to develop or adapt statistical methods for solving problems in the health field. Students enjoy extensive library and computer facilities, as well as myriad opportunities for involvement in numerous research activities in the biomedical sciences and clinical research, which often lead to dissertation topics.

The department awards a number of fellowships to recognize academic achievement and support future scholarly success. As teaching and research experience are considered an important aspect of the program, these fellowships include some teaching and research apprenticeship.

Admissions Requirements

While many of the applicants admitted to Columbia’s PhD program in biostatistics have already completed (or are completing) master’s degrees in biostatistics, statistics, or a related field, admission is open to well qualified students holding (or completing) bachelor’s degrees. Those admitted with a bachelor’s degree are typically strong students from programs that emphasize a rigorous background in mathematics and/or statistics.

Depending on prior training and background, students may be required to take additional master’s level course work in the Mailman School of Public Health as part of their PhD training.

In addition to the requirements listed below, all applicants must submit an official transcript from each prior institution, a statement of academic purpose, and three letters of evaluation from academic sources. All international students whose native language is not English or whose undergraduate degree is from an institution in a country whose official language is not English must submit Test of English as a Foreign Language (TOEFL) or IELTS scores.

• Deadline for Fall Admission: December 1
• Deadline for Spring Admission: No spring admission
• Resume/CV: Yes
• Writing Sample: No
• GRE General: Optional
• GRE Subject: No

View competencies, course requirements, sample schedules, and more in our Academics section.

Paul McCullough, Director of Academic Programs

Biomedical Graduate Studies

Epidemiology and biostatistics graduate group.

• Epidemiology

PhD Program in Epidemiology

The mission of the PhD Program in Epidemiology is to train independent researchers in the development and application of epidemiologic methods and to prepare them for positions as scientific leaders in academia and industry. The PhD is a research degree; it indicates the highest attainable level of scholarship, and a commitment to a research career. The PhD does not represent merely the accumulation of course credits, but rather, the development and completion of a well-designed and conscientious program of scientific investigation that makes a unique contribution to the field of epidemiology.  

The PhD program in Epidemiology requires basic and advanced courses in epidemiology, statistical methods, as well as electives drawn from other departments and schools that serve the student's research interests. The program also requires separate oral qualifications and candidacy examinations, and the successful public defense of a doctoral dissertation, in accordance with University of Pennsylvania policy.  

The PhD program typically requires the equivalent of at least four years of full-time study, in three defined phases:  coursework, pre-candidacy, and candidacy . The coursework phase typically takes two years of full time study, and is intended to provide the student with the knowledge needed to pursue advanced, independent study and investigation in epidemiologic research. This phase culminates in the oral  Qualifications Examination , normally taken after most or all of the student's coursework has been completed. The pre-candidacy phase focuses on the preparation of a scientifically unique, methodologically sound, and feasible dissertation proposal. This phase ends with passing the oral  Candidacy Examination, at which time the student is recognized as a Candidate for the PhD and focuses his or her effort on performing the research for and writing the dissertation. A successful public defense of the dissertation then completes the academic requirements for the PhD.  

The PhD Program in Epidemiology is administered by the Graduate Group in Epidemiology and Biostatistics (GGEB) and is led by the Chair of the Doctoral Program in Epidemiology, working with the PhD Program Executive Committee. The Office of  Biomedical Graduate Studies  (BGS) provides oversight and coordination for the GGEB and six other graduate groups offering PhD degrees in the biomedical sciences. BGS provides centralized support to the graduate groups for admissions, student fellowships, curricular oversight, record-keeping, and other operations.

The current standard course sequence for PhD students consists of up to seven core courses (see below). Additional course units are taken in electives (advanced epidemiology and/or biostatistics courses and courses outside the department and school, as needed to serve the student’s specific interests). In addition, a minimum three semesters of lab rotations (EPID 699) and one unit of dissertation research (EPID 995) are required. They are:

• EPID 7010: Introduction to Epidemiologic Research, 1.0cu
• EPID 7020: Advanced Topics in Epidemiologic Research 1.0cu
• EPID 6000: Data Science for Biomedical Informatics 1.0cu
• BSTA 6300: Statistical Methods and Data Analysis I*  1.0cu
• BSTA6320: Statistical Methods for Categorical and Survival Data (Methods II)* 1.0cu
• EPID 5340: Qualitative Methods in the Study of Health, Disease and Medical Systems, 1.0cu
• HPR 6080: Applied Regression Analysis for Health Policy Research*  1.0cu
• EPID 7000: Doctoral Seminar 1.0cu
• Ethics course 1.0cu (or MSCE workshops)

* Students can either take BSTA 6300 and BSTA 6320  OR HPR 6080.  They do not need to take all three. 

Additional requirements include:

• Participation in a monthly Career Development Workshop Series
• Attendance at the weekly epidemiology seminars
• Participation in a weekly Works in Progress (WIP) session
• Participation in the Responsible Conduct of Research Course
• Completion of web-based seminars in CITI and HIPAA training
• Teaching support as a Teaching Assistant (TA) for an Epidemiology or Biostatistics course
• Successful completion of all PhD examinations

Course descriptions can be found at:  http://www.cceb.med.upenn.edu/course-descriptions

All students are expected to develop and maintain a current course plan developed with and monitored by their mentor. This course plan must be approved by the Program Chair, and will be reviewed semi-annually in order to monitor the student's progress and identify potential delays in completing the program. A typical course plan is provided below.

• Degrees Offered

PhD in Biostatistics

Description.

The doctoral program in Biostatistics trains future leaders, highly qualified as independent investigators and teachers, and who are well-trained practitioners of biostatistics. The program includes coursework in biostatistics, statistics, and one or more public health or biomedical fields. In addition, successful candidates are required to pass PhD applied and theory exams and write a dissertation that reports the results of new biostatistical research undertaken by the candidate.

Likely Careers

Clinical medicine, epidemiologic studies, biological laboratory and field research, genetics, environmental health, health services, ecology, fisheries and wildlife biology, agriculture, and forestry.

Applicants usually have a degree in mathematics, statistics, or a biological field. All applicants should have the equivalent of 30 or more quarter credits in mathematics and statistics, including linear algebra, probability theory, and approximately 2 years of calculus.

Concurrent Option:    PhD/MD

Application Deadline:   Dec 1 - Autumn Quarter Entry

Competencies

Upon satisfactory completion of the PhD in Biostatistics, graduates will be able to:

• Meet the  learning objectives of the MS program in Biostatistics ;
• Recommend and defend appropriate choices of methods to analyze independent outcome data; 
• Implement non-standard statistical methods accurately and efficiently; 
• Provide rigorous proofs characterizing the properties of standard statistical methods;
• Consult effectively with other scientists, addressing statistical issues in the design and analysis of public health or biomedical studies; and
• Design and carry out biostatistical research that will propose a new statistical method or will provide new information about the properties of existing methods.

Learning objectives for the PhD program in Biostatistics in the Generic Pathway:  Upon satisfactory completion of the PhD program in Biostatistics in the Generic Pathway, graduates will be able to:

• Recommend and defend appropriate choices of methods to analyze longitudinal, clustered and other non-independent outcome data; 
• Develop expertise in an area of biostatistical methodology; explain the strengths and weakness of different statistical methods in that area; and
• Explain both orally and in writing how advanced statistical methods work, assessing their strengths and limitations, and the place of particular methods in the larger statistical literature.

Master of Science in Health Data Science

Leverage your skills in statistics, computer science & software engineering and begin your career in the booming field of health data science

The Master of Science (SM) in Health Data Science is designed to provide rigorous quantitative training and essential statistical and computing skills needed to manage and analyze health science data to address important questions in public health and biomedical sciences.

The 16-month program blends strong statistical and computational training to solve emerging problems in public health and the biomedical sciences. This training will enable students to manage and analyze massive, noisy data sets and learn how to interpret their findings. The program will provide training in three principal pillars of health data science: statistics, computing, and health sciences.

Students in the program will learn to:

• Wrangle and transform data to perform meaningful analyses
• Visualize and interpret data and effectively communicate results and findings
• Apply statistical methods to draw scientific conclusions from data
• Utilize statistical models and machine learning
• Apply methods for big data to reveal patterns, trends, and associations
• Employ high-performance scientific computing and software engineering
• Collaborate with a team on a semester-long, data driven research project

The SM in Health Data Science is designed to be a terminal professional degree, giving students essential skills for the job market. At the same time, it provides a strong foundation for students interested in obtaining a PhD in biostatistics or other quantitative or computational science with an emphasis in data science and its applications in health science.

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• Current Students

Biostatistics PhD

Many issues in the health, medical and biological sciences are addressed by collecting and exploring relevant data. The development and application of techniques to better understand such data is a fundamental concern of our program.

This program offers training in the theory of statistics and biostatistics, computer implementation of analytic methods and opportunities to use this knowledge in areas of biological/medical research. The resources of Berkeley Public Health and the UC Berkeley Department of Statistics, together with those of other university departments, offer a broad set of opportunities to satisfy the needs of individual students. Furthermore, the involvement of UCSF faculty from the Department of Biostatistics and Epidemiology also enriches instructional and research activities.

A PhD degree in Biostatistics requires a program of courses selected from biostatistics, statistics, and at least one other subject area (such as environmental health, epidemiology, or genomics), an oral qualifying examination, and a dissertation. Courses cover traditional topics as well as recent advances in biostatistics and statistics. Those completing the PhD will have acquired a deep knowledge and understanding of the MA subject areas. Since graduates with doctorates often assume academic research and teaching careers, a high degree of mastery in research design, theory, methodology, and execution is expected, as well as the ability to communicate and present concepts in a clear, understandable manner.

The PhD degree program requires 4–6 semesters of coursework, the completion of the qualifying examination and dissertation (in total, a minimum of four semesters of registration is required). Biostatistics PhD students are required to take the following classes:

• PBHLTH W200 Foundations of Public Health Practice (Required for students who do not have a Master’s or Doctoral degree from an accredited School of Public Health)
• STAT 210A Theoretical Statistics or STAT 210B Theoretical Statistics
• PBHLTH C240A Introduction to Modern Biostatistics Theory and Practice or PBHLTH C240B Biostatistical Methods: Survival Analysis and Causality
• PBHLTH 252D Introduction to Causal Inference or PBHLTH W252A Introduction to Causal Inference for Public Health Professionals or PBHLTH 252E Advanced Topics in Causal Inference
• PBHLTH 293 Biostatistics Doctoral Seminar

Qualifications

A Master’s degree in Biostatistics or a related field is recommended but not required for admission to the PhD program. Strongly recommended prerequisite courses are calculus, linear algebra, and statistics. Applicants admitted without a Master’s degree may be required to go through the Biostatistics MA curriculum; students can concurrently earn that degree with no additional cost or time to degree. Normative time to degree is 5 years.

Students entering with a relevant master’s degree in biostatistics or statistics must have a faculty advisor who is a member of the Biostatistics Graduate Group committing funding and mentorship support.

GRE Exemption Criteria

GRE General Test scores are required for admission to the Biostatistics PhD program however applicants are exempted from the requirement if they meet all of the following criteria:

• Completed two semesters of calculus for a letter grade and earned a grade of “B” or higher.
• Completed one semester of linear algebra for a letter grade and earned a grade of “B” or higher.
• Completed one semester of statistics for a letter grade and earned a grade of “B” or higher.
• Cumulative undergraduate GPA of 3.0 or higher.
• Overall quantitative/math GPA of 3.0 or higher.
• For students with a Master’s in Biostatistics or a related field, graduate GPA of 3.0 or higher.
• For international students: TOEFL score of 100 or higher OR IELTS score of 7.0 or higher.

Berkeley Public Health also exempts applicants who already hold a doctoral level degree from the GRE requirement.You can find more information on the application instructions page . There is a program page in the Berkeley Graduate Application where you can indicate you meet the criteria for GRE exemption. Applicants who are exempted from the GRE are not at a disadvantage in the application review process.

Many doctoral graduates accept faculty positions in schools of public health, medicine, and statistics and/or math departments at colleges and universities, both in the United States and abroad. Some graduates take research positions, including with pharmaceutical companies, hospital research units, non-profits, and within the tech sector.

Funding and Fee Remission

Prospective students who are US citizens or permanent residents can find more information about applying for an application fee waiver for the Berkeley Graduate Application. Fees will be waived based on financial need or participation in selected programs described on the linked website. International applicants (non-US citizens or Permanent Residents) are not eligible for application fee waivers.

All PhD students are fully funded (including tuition and fees and a stipend or salary) with the exception of Non-Resident Supplemental Tuition (NRST) for the second year, if applicable. NRST is typically waived after the first year of study for PhD students when they advance to candidacy. Information on applying to GSI positions for biostatistics students can be found in the Biostatistics Division student handbook .

Tuition and fees change each academic year. To view the current tuition and fees, see the fee schedule on the Office of the Registrar website (in the Graduate: Academic section).

Please contact [email protected] if you have any questions about funding opportunities for the biostatistics programs.

Diversity, Equity and Inclusion

The Division of Biostatistics is committed to challenging systemic inequities in the areas of health, medical, and biological sciences, and to advancing the goals of diversity, equity, and inclusivity in Biostatistics and related fields.

Diversity, Equity and Inclusion in Biostatistics

Admissions Statistics

Emeritus faculty, faculty associated in biostatistics graduate group.

• Peter Bickel PhD Statistics
• David R. Brillinger PhD Statistics
• Perry de Valpine PhD Environmental Science, Policy, and Management
• Haiyan Huang PhD Statistics
• Michael J. Klass PhD Statistics
• Priya Moorjani PhD Molecular & Cell Biology
• Rasmus Nielsen PhD Integrative Biology and Statistics
• Elizabeth Purdom PhD Statistics
• Sophia Rabe-Hesketh PhD Education
• John Rice PhD Statistics
• Yun S. Song PhD Statistics; Electrical Engineering and Computer Sciences
• Bin Yu PhD Statistics

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Statistics PhD Program

The application for Fall 2025 entry will open Summer 2024

Statistics Program

The Department of Biostatistics and Computational Biology at the University of Rochester conducts teaching and research in statistical theory and methodology oriented toward the health sciences. Our unique graduate program is located within a School of Medicine environment and provides many opportunities for stimulating interaction with applied research.

The department interprets the term "statistics" very broadly, with specialization available in probability, statistical theory and analysis, biostatistics, and interdisciplinary areas of application. Department faculty participate fully in graduate teaching and individual attention is given to each student through intensive advising, extensive small seminars, and research collaboration. Students have opportunities for supervised teaching and statistical consulting experience. Prior to completing their degrees, most Ph.D. students have several publications underway based on research done in collaboration with faculty members in biostatistics/statistics and in various medical departments.

If you are looking for the Undergraduate Program in Statistics, please visit the School of Arts and Sciences .

Doctoral Students

Program Faculty

Year average time for Ph.D. completion

Job placement for Ph.D. graduates over the past 10 years

What Sets Us Apart

Program highlights.

From our highly skilled mentoring faculty to the Bioinformatics and Computational Biology Track, see what sets our program apart

Details of class schedules and full course descriptions can be found on our curriculum page

Mentor Relationships

Our students most commonly reference the personal relationships and valuable mentoring they receive as one of the top reasons why they would recommend URMC

Degree Programs

Ph.D. in Statistics

The Ph.D. in Statistics program offers students a thorough grounding in statistical theory, which provides the necessary foundation for the successful conduct of research in statistical methodology.  

Ph.D. in Statistics with Concentration in Bioinformatics & Computational Biology

The Ph.D. in Statistics with Concentration in Bioinformatics and Computational Biology is designed to educate the next generation of biostatisticians with the knowledge required to address critical scientific and public health questions.

Master of Arts in Statistics

The M.A. in Statistics program is designed for students seeking preparation for a Ph.D. program or work as a Master’s-level statistician.  

Master of Science in Biostatistics

The M.S. in Biostatistics program is primarily intended for students who wish to follow careers in health-related professions such as those in the pharmaceutical industry and biomedical or clinical research organizations.

What Our Students Say

“I chose UR because I am excited by the opportunities available to students both during the program and after completing the program. Graduates from UR have gone on to do wildly different things in academia and industry. It is exciting to have every opportunity available to you, and UR certainly offers that in an environment that supports you in whatever path you choose to take.”  “My favorite things about the program are the people I get to be surrounded by. My classmates are all brilliant and kind people that are a joy to learn alongside. The faculty have a wide range of research interests, which gives students the opportunity to truly carve out the career they want and are devoted to helping students do so.”

Luke McHan, Statistics PhD student

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MD/PhD Statistics

Tufts MD-PhD Program has a long-standing tradition of providing excellent and highly integrated training. The information below gives a snap-shot of our program.

• Number of Applicants for Class Entering in 2022 - 369
• Number of Matriculating - 4
• Average GPA - 3.7
• Current Enrollment - 38
• Average Time to Degree (entering 2003 and later) -  8 years
• Percent completing of both degrees (entering 2003 or later) - 95%
• Percent completing MD (past decade) - 100%

Career Choices of Graduates

• 93% Enter Residency Training
• 76% Pursue Academic Careers
• 8% Pursue Careers Related to the Biotechnology & Pharmaceutical Industry

• School of Health Professions
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• University of Kansas
• The University of Kansas Health System
• The University of Kansas Cancer Center

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Ph.D. in Biostatistics

KU's Ph.D. in Biostatistics program is nationally regarded for producing highly skilled biostatisticians prepared to collaborate or lead in a variety of fields.

The Ph.D. in Biostatistics at KU Medical Center is a 63-credit-hour program, recognized for the quality, ability and training of its graduating students. This fully accredited program includes collaborative research experience, annual evaluations, graduate examinations and the successful completion of a doctoral dissertation. Dissertation research culminates in a final dissertation examination which consists of an oral presentation by the candidate and an examination by the faculty. 

The goal of the Ph.D. program is to produce biostatisticians who can develop biostatistical methodologies that can be utilized to solve problems in public health and the biomedical sciences. In addition, graduates will be prepared to apply biostatistical and epidemiology methodology for the design and analysis of public health and biomedical research investigations. Finally, graduates will be well-suited to function as collaborators or team leaders on research projects in the biomedical and public health sciences.

Download the Guide (PDF)

Upon completion of KU's Ph.D. in Biostatistics program, graduates will have the characteristics outlined for master's degree graduates and will possess the following additional skills: 

• The ability to develop careers in academia, research, institutes, government and industry.
• A broad understanding of current statistical methods and practices in health science.
• A solid theoretical training necessary for the development and study of new statistical methods. 
• The ability to assume all responsibilities of a statistician in collaborative health science research. In particular, the graduate will have experience in the design, data management, analysis and interpretation of a variety of experimental and observational studies. 

University of Kansas Medical Center Department of Biostatistics & Data Science 3901 Rainbow Boulevard Mailstop 1026 Kansas City, KS 66160

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Statistics PhD Program

Student opportunities, scientific conferences and meetings.

The Department of Biostatistics and Computational Biology recognizes the role of participation in meetings and conferences in the professional development of our doctoral students and strongly encourages such participation. Each student may apply for a maximum of $1,500 in support of travel expenses from central departmental funds over the course of their doctoral studies. Funds obtained from other sources, including awards provided directly by conference organizers, will be considered supplemental. Students are also encouraged to apply for travel awards in the School of Medicine and Dentistry.

• Amazon Graduate Research Symposium
• American Causal Inference Conference (ACIC)
• American Statistical Association Conference on Statistical Practice (CSP)
• Bioconductor Conference: Where Software and Biology Connect (BioC)
• Conference on Lifetime Data Science (LIDA)
• Eastern North American Region of The International Biometric Society Meeting (ENAR)
• Global Symposium of Innovation in Trauma Research Methods
• Great Lakes Bioinformatics and the Canadian Computational Biology Conference (GLBIO/CCBC)
• International Chinese Statistical Association Applied Statistics Symposium (ICSA)
• International Conference of the ERCIM Working Group on Computational and Methodological Statistics (CMStatistics)
• International Conference on Big Data and Information Analytics (BigDIA)
• International Conference on Computational and Financial Econometrics (CFE)
• International Society for Bayesian Analysis World Meeting (ISBA)
• International Society for Computational Biology Conference (ISCB)
• International Society for Computerized Electrocardiology Annual Conference (ISCE)
• Italian Statistical Society Classification and Data Analysis Group (CLADAG)
• Joint Conference of the Upstate Chapters of the American Statistical Association (UP-STAT)
• Joint Statistical Meetings (JSM)
• New England Statistics Symposium (NESS)
• Penn Conference on Big Data in Biomedical and Population Health Sciences
• Society for Epidemiologic Research Annual Meeting
• Statistical and Applied Mathematical Sciences Institute Workshop (SAMSI)
• Summer Institute in Statistical Genetics (SISG)
• Western North American Region of The International Biometric Society Meeting  (WNAR)
• Women in Statistics and Data Science Conference (WSDS)

Summer Internships

PhD students may complete external internships during the summer. Students are encouraged to review the free professional development resources available through the school’s myHub before applying for internships. Career services offered include one-on-one counseling on interviewing skills, networking strategies, and developing strong CVs, resumes, and cover letters. International students studying on the F-1 visa will need to contact the University's International Services Office (ISO) for Curricular Practical Training (CPT) instructions.

• AbbVie (Illinois)
• Allergan (California)
• Amgen (California)
• Argonne National Laboratory (Illinois)
• Bank of America (North Carolina)
• Bayer Healthcare Pharmaceuticals (New Jersey)
• Biogen (Massachusetts)
• Bristol Myers Squibb (remote)
• Capital One (Texas)
• Chevron Oronite Company (California)
• CNA Insurance (Illinois)
• Constellation Brands (New York)
• DuPont Company (Delaware)
• EY Ernst & Young (New York)
• Excellus BlueCross BlueShield (New York)
• Genentech (California)
• Google (California)
• Institute for Defense Analyses (Virginia)
• Janssen Research & Development (New Jersey)
• Johnson & Johnson (remote)
• Liberty Mutual Insurance (Massachusetts)
• Mayo Clinic (Minnesota)
• Medivation (California)
• Merck & Co. (Pennsylvania)
• National Institute of Child Health and Human Development (Maryland)
• Novartis Pharmaceuticals (New Jersey)
• PharmaLex (Massachusetts)
• Quantarium (Washington)
• Regeneron Pharmaceuticals (New York)
• SAS Institute (North Carolina)
• Takeda Pharmaceuticals (Massachusetts)
• Travelers Insurance (Connecticut)
• U.S. Food and Drug Administration (Maryland)
• Vertex Pharmaceuticals (Massachusetts)

Statistical Consulting Service

The Department of Biostatistics and Computational Biology offers a Statistical Consulting Service with services ranging from purely advisory assistance to complete mathematical/statistical analysis and data management support for projects. Faculty members are available to serve as research collaborators and statistical consultants, with support from research associates and graduate research assistants. Doctoral students in their third year or higher will assist with consulting requests on a rotating schedule by attending consulting meetings with the faculty on-call and completing work requested by faculty for the research project.

Environmental Health Biostatistics Training Grant

Biostatistics and Computational Biology Department Seminars

The department has a colloquium series in which both senior and junior faculty from around the country travel to Rochester to give a seminar and meet with faculty and students. The department also hosts an annual lecture by a distinguished statistician in honor of Dr. Charles L. Odoroff, the founding Director of the Division of Biostatistics (now the Department of Biostatistics and Computational Biology) at the University of Rochester, and another annual lecture in honor of Dr. Andrei Yakovlev, who as Chair led a substantial expansion of the department, particularly in the areas of bioinformatics and computational biology.

All department members are invited to attend meetings on a variety of topics related to diversity, equity, and inclusion . The meetings are intended to raise awareness, provide a forum to discuss issues related to these topics, and provide an opportunity to learn from other people's stories and perspectives. Most meetings are open only to department members, and generally consist of watching a short video followed by small group discussions and concluding with a short discussion with the whole group. Topics have included microaggression and implicit bias. Some meetings are replaced by seminars given by outside speakers and are open to a wider audience. Topics of outside seminars have included hidden curriculum and pipeline programs for undergraduates to support diversity.

Annual Statistics PhD Student Workshop

The Annual Statistics PhD Student Workshop includes presentations by all PhD students in the department who have passed the Advanced Examination but have not yet passed their Proposal (Qualifying) Examination. The primary purpose of the Workshop is to allow students to obtain feedback on their research ideas from the entire program faculty, whose perspectives may be somewhat different from that of the primary research advisor and potentially quite valuable. The Workshop also provides additional experience for each student in preparing and delivering a presentation on a topic of their particular interest.

Future Faculty Workshops

Students who are considering a career in academia are invited to attend Future Faculty Workshops sponsored by the offices of the Provost and Faculty Development and Diversity. These workshops offer information and hands-on experience with aspects of faculty life not traditionally part of graduate curriculums. The series aims to prepare the next generation of faculty and to give University of Rochester graduates a competitive edge in the academic marketplace.

Scientific Communication and Leadership Courses

The school offers several optional interdepartmental (IND) courses related to scientific communication and leadership, including:

• IND 414 Scientific Writing: Principles and Practice
• IND 417 Workshop in Scientific Communications
• IND 420 Mastering Scientific Information
• IND 426 Scientific Communication for Diverse Audiences
• IND 438 Practical Skills in Grant Writing
• IND 439 Leadership and Management for Scientists
• IND 442 Science Outreach to All

Graduate Student Groups

The Graduate Student Society (GSS) maintains a list of current student-run and University of Rochester organizations open to School of Medicine and Dentistry graduate student membership. The 2023-2024 GSS department representative is 4 th year PhD student Luke McHan.

The Rochester Data Science Society (RDSS) serves all students at the University who are interested in data science, statistics, computer science, engineering, health analytics, economics, and other related fields. The society was established in 2017 by Shiyang Ma (MA '15, PhD '19), along with students from the Health Services Research, Epidemiology, and Computer Science graduate programs.

• 2023 FACTS: Applicants and Matriculants Data

2023 FACTS: Enrollment, Graduates, and MD-PhD Data

• 2023 FACTS: Electronic Residency Application Service (ERAS) Data
• FACTS Glossary

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Summary Data

Additional Resources

Graduation Rates and Attrition Rates of U.S. Medical Students (PDF) This AAMC Data Snapshot provides information on the graduation and attrition rates of U.S. Medical Students.

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Population Health Sciences PhD, MScR

Awards: PhD, MScR

Study modes: Full-time, Part-time

Funding opportunities

Programme website: Population Health Sciences

Upcoming Introduction to Postgraduate Study session

Join us on the 26th June to learn more about studying at the University of Edinburgh.

Find out more and register

Research profile

The Usher Institute supervises postgraduate research students in a wide range of population health disciplines, including epidemiology, genetic epidemiology, health promotion, health services research, medical statistics, molecular epidemiology and sociology and on a wide range of topics including allergic and respiratory disease, clinical trial and statistics methodology, eHealth, ethnicity and health, genetic epidemiology of complex diseases, global health, palliative care and cancer, society and health and families and relationships.

A principal aim is to foster interdisciplinary research involving quantitative and qualitative approaches via effective collaboration with biomedical scientists, epidemiologists, social scientists and clinical researchers throughout the University and beyond.

Before Applying

Before submitting an online application, prospective students should contact an academic members of staff who may act as first supervisor in order to align their research proposal with one of the Institute's main areas of research. A list of contacts for PhD supervisors can be found at:

• Usher Institute research
• List of supervisors

Training and support

Students will be integrated within the existing student-led approach at the Usher Institute, where structures are already in place to ensure a high-quality student experience.

The Centre for Population Health Sciences, which forms a large part of the Usher Institute, has a thriving PhD community with well-developed management and administrative structures.

University Quality Assurance monitoring and reporting processes will be adhered to. All supervisors will satisfy University requirements in terms of training and mentoring.

Expectations on the students, including assessment guidelines, will be clearly communicated by multiple channels (e.g. at interview, during induction, in the Postgraduate Research Student and Supervisor Handbook, by supervisors, at annual review meetings and on relevant web pages). All students will have at least two supervisors who will also give pastoral care and career advice in addition to student services provision.

Students will attend appropriate training, including transferable skills, at appropriate courses (e.g. from the Institute of Academic Development) identified in consultation with the supervisors.

The Usher Institute brings together researchers active in population health science research, including public health and primary care.

Within the school the Usher academic staff play a large role in research project supervision.

There are also links with the Institute of Genetics and Cancer and the Queen's Medical Research Institute.

Entry requirements

These entry requirements are for the 2024/25 academic year and requirements for future academic years may differ. Entry requirements for the 2025/26 academic year will be published on 1 Oct 2024.

MSc by Research: A UK 2:1 honours degree, or its international equivalent.

PhD: A UK 2:1 honours degree and a UK masters degree with a mark of at least 60%, or their international equivalents. We will also consider a UK 2:1 honours degree, or its international equivalent, and significant work experience in an area relevant to your research project.

International qualifications

Check whether your international qualifications meet our general entry requirements:

• Entry requirements by country
• English language requirements

Regardless of your nationality or country of residence, you must demonstrate a level of English language competency at a level that will enable you to succeed in your studies.

English language tests

We accept the following English language qualifications at the grades specified:

• IELTS Academic: total 6.5 with at least 6.0 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
• TOEFL-iBT (including Home Edition): total 92 with at least 20 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
• C1 Advanced ( CAE ) / C2 Proficiency ( CPE ): total 176 with at least 169 in each component.
• Trinity ISE : ISE II with distinctions in all four components.
• PTE Academic: total 62 with at least 59 in each component.

Your English language qualification must be no more than three and a half years old from the start date of the programme you are applying to study, unless you are using IELTS , TOEFL, Trinity ISE or PTE , in which case it must be no more than two years old.

Degrees taught and assessed in English

We also accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English speaking country, as defined by UK Visas and Immigration:

• UKVI list of majority English speaking countries

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English speaking countries (non-MESC).

• Approved universities in non-MESC

If you are not a national of a majority English speaking country, then your degree must be no more than five years old* at the beginning of your programme of study. (*Revised 05 March 2024 to extend degree validity to five years.)

Find out more about our language requirements:

Fees and costs

Tuition fees.

Scholarships and funding

Featured funding.

• Usher Institute postgraduate funding and scholarships

UK government postgraduate loans

If you live in the UK, you may be able to apply for a postgraduate loan from one of the UK's governments.

The type and amount of financial support you are eligible for will depend on:

• your programme
• the duration of your studies
• your tuition fee status

Programmes studied on a part-time intermittent basis are not eligible.

• UK government and other external funding

Other funding opportunities

Search for scholarships and funding opportunities:

• Search for funding

Further information

• Postgraduate Administrator, Sarah Golightly
• Phone: +44 (0)131 651 5446
• Contact: [email protected]
• Usher Institute
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• Programme: Population Health Sciences
• School: Edinburgh Medical School: Molecular, Genetic & Population Health Sciences
• College: Medicine & Veterinary Medicine

Select your programme and preferred start date to begin your application.

PhD Population Health Sciences - 3 Years (Full-time)

Phd population health sciences - 6 years (part-time), msc by research population health sciences - 1 year (full-time), msc by research population health sciences - 2 years (part-time), application deadlines.

We encourage you to apply at least one month prior to entry so that we have enough time to process your application. If you are also applying for funding or will require a visa then we strongly recommend you apply as early as possible.

• How to apply

You must submit two references with your application.

Before making your application, it is advisable to make contact with a potential supervisor to discuss your research proposal. Further information on making a research degree application can be found on the College website:

• How to apply for a research degree

You will be formally interviewed (in person, by video-conferencing or Skype).

Find out more about the general application process for postgraduate programmes:

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PhD in Biostatistics

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The MRC Biostatistics Unit is an internationally recognised research department of the University of Cambridge specialising in statistical modelling with application to medical, biological or public health sciences.

Our PhD students are registered with the University of Cambridge. Students belong to one of the University's Colleges and are trained at our Unit at the University Forvie Site on the Cambridge Biomedical Campus at Addenbrooke's Hospital.

We maintain strong links with the University of Cambridge Statistical Laboratory, Alan Turing Institute and other mathematical departments (who are based in the Centre for Mathematical Sciences on the West Cambridge site).

Those who wish to progress to a PhD after completing an MPhil will be required to satisfy their potential supervisor, Head of Department and the Faculty Degree Committee that they have the skills and ability to achieve the higher degree.

The Postgraduate Virtual Open Day usually takes place at the end of October. It’s a great opportunity to ask questions to admissions staff and academics, explore the Colleges virtually, and to find out more about courses, the application process and funding opportunities. Visit the  Postgraduate Open Day  page for more details.

See further the  Postgraduate Admissions Events  pages for other events relating to Postgraduate study, including study fairs, visits and international events.

Key Information

3-4 years full-time, 4-7 years part-time, study mode : research, doctor of philosophy, mrc biostatistics unit, course - related enquiries, application - related enquiries, course on department website, dates and deadlines:, lent 2024 (closed).

Some courses can close early. See the Deadlines page for guidance on when to apply.

Easter 2024 (Closed)

Michaelmas 2024 (closed), easter 2025, funding deadlines.

These deadlines apply to applications for courses starting in Michaelmas 2024, Lent 2025 and Easter 2025.

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• v.6(2); Winter 2006

Clinicians' Guide to Statistics for Medical Practice and Research: Part I

Marie a. krousel-wood.

* Ochsner Clinic Foundation, New Orleans, Louisiana

† Department of Epidemiology, Tulane School of Public Health and Tropical Medicine, New Orleans, Louisiana

Richard B. Chambers

Paul muntner, introduction.

This two-part series will present basic statistical principles for the practicing physician to use in his or her review of the literature and to the physician engaged in clinical research. The purpose of this series is threefold: (1) to provide an overview of common epidemiological and statistical terms and concepts that can be useful to the practitioner and clinical researcher, (2) to review calculations for common epidemiological measures and statistical tests, and (3) to provide examples from the published literature of uses of statistics in medical care and research. This review is not intended to be a comprehensive presentation of epidemiology or statistics since there are already a number of excellent sources for this information 1–6), but rather as a quick reference for practical application of statistical principles and concepts in medical care and clinical research.

In this issue, Part I of the Series is presented and includes discussion of the study question, study goals, appropriate study design, and appropriate statistical tests.

Physicians can be overwhelmed when reviewing published and current studies to determine what is relevant to their clinical practice and/or clinical research. Some initial questions outlined below may guide the process for reviewing an article or setting up a clinical study.

• What is the study question? What are the study goals?
• What is the appropriate study design to answer the study question?
• What are the appropriate statistical tests to utilize?

What Is the Study Question? What Are the Study Goals?

Whether in clinical practice or in a clinical research “laboratory,” physicians often make observations that lead to questions about a particular exposure and a specific disease. For example, one might observe in clinical practice that several patients taking a certain antihypertensive therapy develop pulmonary symptoms within 2 weeks of taking the drug. The physician might question if the antihypertensive therapy is associated with these symptoms. A cardiologist may observe in a review of the medical literature that the initial costs of caring for patients with cardiovascular diseases have been reported to be greater if the patient is cared for by a specialist than if the patient is cared for by a non-specialist. Because the physician may believe that although initial costs are greater, the follow-up costs are less, he or she may question if there would be a difference by specialist versus non-specialist if all costs were assessed. Questions like these can lead to formal hypotheses that can then be tested with appropriate research study designs and analytic methods. Identifying the study question or hypothesis is a critical first step in planning a study or reviewing the medical literature. It is also important to understand up front what the related study goals are. Some questions that may facilitate the process of identifying the study goals follow:

• Is the goal to determine:
• – how well a drug or device works under ideal conditions (i.e., efficacy)?
• – how well a drug or device works in a free-living population (i.e., effectiveness)?
• – the causes or risk factors for a disease?
• – the burden of a disease in the community?
• Is the study goal to provide information for a quality management activity?
• Will the study explore cost-effectiveness of a particular treatment or diagnostic tool? The hypotheses and the goals of a study are the keys to determining the study design and statistical tests that are most appropriate to use.

What Is the Appropriate Study Design To Answer the Study Question?

Once the study question(s) and goals have been identified, it is important to select the appropriate study design. Although the key classification scheme utilizes descriptive and analytic terminology, other terminology is also in vogue in evaluating health services and will be briefly described at the end of this section.

Classification Schemes

Epidemiology has been defined as “the study of the distribution and determinants of disease frequency” in human populations (4) . The primary classification scheme of epidemiological studies distinguishes between descriptive and analytic studies. Descriptive epidemiology focuses on the distribution of disease by populations, by geographic locations, and by frequency over time. Analytic epidemiology is concerned with the determinants, or etiology, of disease and tests the hypotheses generated from descriptive studies. Table 1 lists the study design strategies for descriptive and analytic studies. Below is a brief description of the various design strategies. The strengths and limitations of these study designs are compared in Table 2 .

Table 1: Outline of Study Design Strategies for Descriptive and Analytic Studies.

Table 2: Strengths and Limitations of Descriptive and Analytic Study Designs*

Descriptive Studies:

Correlational studies, also called ecologic studies, employ measures that represent characteristics of entire populations to describe a given disease in relation to some variable of interest (e.g. medication use, age, healthcare utilization). A correlation coefficient (i.e. Pearson's “r”; Spearman's “T”; or Kendall's “K”) quantifies the extent to which there is a linear relationship between the exposure of interest or “predictor” and the disease or “outcome” being studied. The value of the coefficient ranges between positive 1 and negative 1. Positive 1 reflects a perfect correlation where as the predictor increases, the outcome (or risk of outcome) increases. Negative 1 reflects a perfect inverse correlation where as the predictor increases the outcome (or risk of outcome) decreases. An example of a correlation study would be that of St Leger and colleagues who studied the relationship between mean wine consumption and ischemic heart disease mortality (7) . Across 18 developed countries, a strong inverse relationship was present. Specifically, countries with higher wine consumption had lower rates of ischemic heart disease and countries with lower wine consumption had higher rates of ischemic heart disease. Although correlation studies provide an indication of a relationship between an exposure and an outcome, this study design does not tell us whether people who consume high quantities of wine are protected from heart disease. Thus, inferences from correlation studies are limited.

Case reports and case series are commonly published and describe the experience of a unique patient or series of patients with similar diagnoses. A key limitation of the case report and case series study design is the lack of a comparison group. Nonetheless, these study designs are often useful in the recognition of new diseases and formulation of hypotheses concerning possible risk factors. In a case series study reported by Kwon and colleagues (8) , 47 patients were examined who developed new or worsening heart failure during treatment with tumor necrosis factor (TNF) antagonist therapy for inflammatory bowel disease or rheumatoid arthritis. After TNF antagonist therapy, 38 patients (of which 50% had no identifiable risk factors) developed new-onset heart failure and 9 experienced heart failure exacerbation. From this descriptive study, the authors concluded that TNF antagonist might induce new-onset heart failure or exacerbate existing disease (8) .

Cross-sectional surveys are also known as prevalence surveys. In this type of study, both exposure and disease status are assessed at the same time among persons in a well-defined population. These types of studies have become more common recently with the development and validation of survey tools such as the Short Form 36 (SF 36) functional status questionnaire and the Kansas City Cardiomyopathy Questionnaire (KCCQ) functional status survey. Cross-sectional studies are especially useful for estimating the population burden of disease. The prevalence of many chronic diseases in the United States is calculated using the National Health and Nutrition Examination Survey, an interview and physical examination study including thousands of non-institutionalized citizens of the United States. For example, Ford and colleagues estimated that 47 million Americans have the metabolic syndrome using the Third National Health and Nutrition Examination Survey (9) . Of note, in special circumstances where one can easily deduce an exposure variable preceding the outcome or disease, cross sectional surveys can be used to test epidemiologic hypotheses and thus can be used as an analytic study. For example, Bazzano and colleagues used data collected from a cross-sectional study to conclude that cigarette smoking may raise levels of serum C-reactive protein (10) . A cross-sectional study is useful in this situation because it is unlikely that having high levels of C-reactive protein would cause one to smoke cigarettes.

Analytic Studies:

Analytic studies can be observational or experimental. In observational studies, the researchers record participants' exposures (e.g., smoking status, cholesterol level) and outcomes (e.g., having a myocardial infarction). In contrast, an experimental study involves assigning one group of patients to one treatment and another group of patients to a different or no treatment. There are two fundamental types of observational studies: case control and cohort. A case control study is one in which participants are chosen based on whether they do (cases) or do not (controls) have the disease of interest. Ideally, cases should be representative of all persons developing the disease and controls representative of all persons without the disease. The cases and controls are then compared as to whether or not they have the exposure of interest. The difference in the prevalence of exposure between the disease/no disease groups can be tested. In these types of studies, the odds ratio is the appropriate statistical measure that reflects the differences in exposure between the groups.

The defining characteristic of a cohort study, also known as a follow-up study, is the observation of a group of participants over a period of time during which outcomes (e.g., disease or death) develop. Participants must be free from the disease of interest at the initiation of the study. Subsequently, eligible participants are followed over a period of time to assess the occurrence of the disease or outcome. These studies may be classified as non-concurrent/retrospective or concurrent/prospective.

Retrospective cohort studies refer to those in which all pertinent events (both exposure and disease) have already occurred at the time the study has begun. The study investigators rely on previously collected data on exposure and disease. An example of a non-concurrent/retrospective cohort study would be that of Vupputuri and colleagues (11) , who in 1999–2000 abstracted data on blood pressure and renal function from charts for all patients seen at the Veterans Administration Medical Center of New Orleans Hypertension Clinic from 1976 through 1999. They analyzed the data to see if blood pressure at each patient's first hypertension clinic encounter was associated with a subsequent deterioration in renal function.

In prospective studies, the disease/outcome has not yet occurred. The study investigator must follow participants into the future to assess any difference in the incidence of the disease/outcome between the types of exposure. The incidence of the disease/outcome is compared between the exposed and unexposed groups using a relative risk (RR) calculation. The advantages of retrospective cohort studies, relative to prospective, include reduced cost and time expenditures as all outcomes have already occurred. In contrast, the major disadvantage of the non-concurrent/retrospective studies is the reliance on available data that were collected for clinical purposes and, generally, not following a carefully designed protocol. There are two additional sub-classifications for cohort studies. First, cohort studies may include a random sample of the general population, e.g., Framingham and Atherosclerosis Risk in Communities (12–16) or a random sample of a high-risk population (17) . In these latter studies, a sample of all individuals or individuals with a specific demographic, geographic, or clinical characteristic is included. Second, cohort studies may begin by identifying a group of persons with an exposure and a comparison group without the exposure. This type of cohort study is usually performed in the situation of a rare exposure.

Experimental or intervention studies are commonly referred to as clinical trials. In these studies, participants are randomly assigned to an exposure (such as a drug, device, or procedure). “The primary advantage of this feature (ed: randomized controlled trials) is that if the treatments are allocated at random in a sample of sufficiently large size, intervention studies have the potential to provide a degree of assurance about the validity of a result that is simply not possible with any observational design option” (5) . Experimental studies are generally considered either therapeutic or preventive. Therapeutic trials target patients with a particular disease to determine the ability of a treatment to reduce symptoms, prevent recurrence or decrease risk of death from the disorder. Prevention trials involve the assessment of particular therapies on reducing the development of disease in participants without the disease at the time of enrollment. One such prevention trial is the Drugs and Evidence Based Medicine in the Elderly (DEBATE) Study, which has as its primary aim “ to assess the effect of multi-factorial prevention on composite major cardiovascular events in elderly patients with atherosclerotic diseases” (18) .

Other Classification Schemes:

Some other classification schemes in use today are based on the use of epidemiology to evaluate health services. Epidemiological and statistical principles and methodologies are used to assess health care outcomes and services and provide the foundation for evidence-based medicine. There are different ways to classify studies that evaluate health care services. One such scheme distinguishes between process and outcomes studies. Process studies assess whether what is done in the medical care encounters constitutes quality care (e.g. number and type of laboratory tests ordered, number and type of medications prescribed, frequency of blood pressure measurement). An example of a process study would be one that evaluated the percentage of patients with chronic heart failure in a given population who have filled prescriptions for angiotensin converting enzyme inhibitors (ACE – Inhibitors). A criticism of process studies is that although they document whether or not appropriate processes were done, they don't indicate if the patient actually benefited or had a positive outcome as a result of the medical processes.

Outcomes studies assess the actual effect on the patient (e.g. morbidity, mortality, functional ability, satisfaction, return to work or school) over time, as a result of their encounter(s) with health care processes and systems. An example of this type of study would be one that assessed the percentage of patients with a myocardial infarction (MI) who were placed on a beta blocker medication and subsequently had another MI. For some diseases, there may be a significant time lag between the process event and the outcome of interest. This often results in some patients being lost to follow-up, which may lead to erroneous conclusions unless methods that “censor” or otherwise adjust for missing time-dependent covariates are used.

In reviewing the medical literature, one often encounters other terms that deal with the evaluation of medical services: efficacy, effectiveness, or efficiency. Efficacy evaluates how well a test, medication, program or procedure works in an experimental or “ideal” situation. Efficacy is determined with randomized controlled clinical trials where the eligible study participants are randomly assigned to a treatment or non-treatment, or treatment 1 versus treatment 2, group. Effectiveness assesses how well a test, medication, program or procedure works under usual circumstances. In other words, effectiveness determines to what extent a specific healthcare intervention does what it is intended to do when applied to the general population. For example, although certain anti-retroviral therapies work well using direct observed therapy in the controlled setting of a clinical trial (i.e., they are efficacious), once applied to a free-living population, the drug dosing regimen may be too difficult for patients to follow in order to be effective. Finally, efficiency evaluates the costs and benefits of a medical intervention.

What Are the Appropriate Statistical Tests?

Once the appropriate design is determined for a particular study question, it is important to consider the appropriate statistical tests that must be (or have been) performed on the data collected. This is relevant whether one is reviewing a scientific article or planning a clinical study. To begin, we will look at terms and calculations that are used primarily to describe measures of central tendency and dispersion. These measures are important in understanding key aspects of any given dataset.

Measures of Central Tendency

There are three commonly referred to measures of central location: mean, median, and mode. The arithmetic mean or average is calculated by summing the values of the observations in the sample and then dividing the sum by the number of observations in the sample. This measure is frequently reported for continuous variables: age, blood pressure, pulse, body mass index (BMI), to name a few. The median is the value of the central observation after all of the observations have been ordered from least to greatest. It is most useful for ordinal or non-normally distributed data. For data sets with an odd number of observations, we would determine the central observation with the following formula:

For datasets with an even number of observations, we would select the case that was the average of the following observations' values:

The mode is the most commonly occurring value among all the observations in the dataset. There can be more than one mode. The mode is most useful in nominal or categorical data. Typically no more than two (bimodal) are described for any given dataset.

Example 1: A patient records his systolic blood pressure every day for one week. The values he records are as follows: Day 1: 98 mmHg, Day 2: 140 mmHg, Day 3: 130 mmHg, Day 4: 120 mmHg, Day 5: 130 mmHg, Day 6: 102 mmHg, Day 7: 160 mmHg.

The arithmetic mean or average for these 7 observations is calculated as follows:

Table 3: Advantaged and Disadvantages of Measure of Central Tendency and Dispersion*

Measures of Dispersion

Measures of dispersion or variability provide information regarding the relative position of other data points in the sample. Such measures include the following: range, inter-quartile range, standard deviation, standard error of the mean (SEM), and the coefficient of variation.

Range is a simple descriptive measure of variability. It is calculated by subtracting the lowest observed value from the highest. Using the blood pressure data in example 1, the range of blood pressure would be 160 mmHg minus 98 mmHg or 62 mmHg. Often given with the median (i.e., for non-normally distributed data) is the interquartile range, which reflects the values for the observations at the 25th and 75th percentiles of a distribution.

The most commonly used measures of dispersion include variance and its related function, standard deviation, both of which provide a summary of variability around the mean. Variance is calculated as the sum of the squared deviations divided by the total number of observations minus one:

The standard deviation is the square root of the variance. Table 4 presents calculations of variance and standard deviation for the systolic blood pressures given in example 1.

Table 4: Example of a Standard Deviation Calculation

The coefficient of variation (CV) is a measure that expresses the SD as a proportion of the mean:

This measure is useful if the clinician wants to compare 2 distributions that have means of very different magnitudes. From the data provided in example 1, the coefficient of variation would be calculated as follows:

The standard error of the mean (SEM) measures the dispersion of the mean of a sample as an estimate of the true value of the population mean from which the sample was drawn. It is related to, but different from, the standard deviation. The formula is as follows:

Using the data from example 1, the SEM would be:

SEM can be used to describe an interval within which the true sample population mean lies, with a given level of certainty. Which measure of dispersion to use is dependent on the study purpose. Table 3 provides some information which may facilitate the selection of the appropriate measure or measures.

Comparing Central Tendencies with Respect to Dispersions (Error Terms)

Once central tendency and dispersion are measured, it follows that a comparison between various groups (e.g., level of systolic blood pressure among persons taking ACE-Inhibitors versus beta-blockers) is desired. If working with continuous variables that are normally distributed, the comparison is between means. The first step is to simply look at the means and see which is larger (or smaller) and how much difference lies between the two. This step is the basis of deductive inference. In comparing the means, and, preferably before calculating any p-values, the clinician or investigator must answer the question: is the observed difference clinically important? If the magnitude of the observed difference is not clinically important, then the statistical significance becomes irrelevant in most cases. If the observed difference is clinically important, even without statistical significance, the finding may be important and should be pursued (perhaps with a larger and better powered study; Table 5 ).

Clinical Versus Statistical Significance and Possible Conclusions

Once a deductive inference is made on the magnitude of the observed differences, statistical inference follows to validate or invalidate the conclusion from the deductive inference. To illustrate: if two people each threw one dart at a dartboard, would one conclude that whoever landed closer to the center was the more skilled dart thrower? No. Such a conclusion would not be reasonable even after one game or one match as the result may be due to chance. Concluding who is a better player would have to be based on many games, against many players, and over a period of time. There are many reasons for inconsistencies (good day, bad day, etc.), but they all boil down to variance. For a player to be classified as a “good player,” he/she has to be consistently good over time.

Because in clinical research we rely on a sample of the patient population, variance is a key consideration in the evaluation of observed differences. The observed difference between exposed and unexposed groups can be large, but one must consider how it stands next to the variation in the data. Since these parameters are highly quantifiable, the probability that the means are different (or similar) can be calculated. This process takes place in a statistical method called analysis of variance (ANOVA). The details of this process are beyond the scope of this chapter; nevertheless, ANOVA is a fundamental statistical methodology and is found in many texts and is performed by many statistical software packages. In essence, the ANOVA answers the question: are differences between the study groups' mean values substantial relative to the overall variance (all groups together)? It is important to note that even though ANOVA reveals statistically significant differences, the ANOVA does not indicate between which groups the difference exists. Therefore, further analysis with multiple comparison tests must be performed to determine which means are significantly different. Portney and Watkins (19) provide a good overview of these procedures.

In the special case where one and only one comparison can be made, the t-test can be done. It was developed to be a shortcut comparison of only two means between groups with small sample sizes (less than 30). If used for more than one comparison or when more than one comparison is possible, the t-tests do not protect against Type 1 error at the assumed level of tolerance for Type 1 error (usually α = 0.05).

Probability: Fundamental Concepts in Evidence-Based Medicine

Armed with a basic understanding of algebra and user-friendly statistical software, most clinicians and clinical researchers can follow the cookbook method of statistical inference. Problems quickly arise because the vast majority of medical research is not designed as simply as the examples given in basic statistics textbooks nor analyzed as simply as the shortcut methods often programmed beneath the layers of menus in easy-to-use software. Violations of assumptions that are necessary for a classic statistical method to be valid are more the rule than the exception. However, avoiding the misinterpretation of statistical conclusions does not require advanced mastery of the mathematics of probability at the level of calculus. An effort to understand, at least qualitatively, how to measure the degree of belief that an event will occur will go a long way in allowing non-mathematicians to make confident conclusions with valid methods.

Two practical concepts should be understood up front: first, understanding that every probability, or rate, has a quantifiable uncertainty that is usually expressed as a range or confidence interval. Second, that comparing different rates observed between two populations, or groups, must be done relative to the error terms. This is the essence of statistical inference.

Probability Distributions:

The final rate of an event that is measured, as the size of the sample being measured grows to include the entire population, is the probability that any individual in the population will experience the event. For example, one analyzes a database of heart transplant patients who received hearts from donors over the age of 35 to determine the rate of cardiac death within a 5-year post-transplant follow-up period (20) . If the first patient in the sample did not survive the study period, the sample of this one patient gives an estimated cardiac death rate of 100%. No one would accept an estimate from a sample of one. However, as the sample size increases, the event rate will migrate towards truth. If the next patient in the database is a survivor, the cardiac death rate falls to 50%. Once the entire population represented in the database is included in the sample (n=26), it is observed that 7 experienced cardiac death for a final cardiac death rate of 27%. When written as a probability, one can say that the probability is 0.27 that any single participant randomly sampled from this database will be recorded as having a cardiac death within 5 years of receiving a heart transplant. It may be more relevant to use the data to predict that the next patient seen in clinic and added to the database will have a probability of 0.27 of experiencing cardiac death within 5 years. The illustration just described is that of a binomial probability. That is, the outcome is one of two possible levels (binary): survival or death.

For this example, the SE is calculated by:

The Z score for a two sided 95% confidence interval (CI) is 1.96, so the range of the CI is calculated by lower 95% CI = 0.27 − 1.96 × 0.087 = 0.011 and upper 95% CI = 0.27 + 1.96 × 0.087 = 0.440, respectively. These yield the range (.011, .440). Thus, if this observation were repeated 100 times in similar populations of the same sample size, 95 of the sampled death rates would fall between .011 and .440.

Evaluating Diagnostic and Screening Tests

In order to understand disease etiology and to provide appropriate and effective health care for persons with a given disease, it is essential to distinguish between persons in the population who do and do not have the disease of interest. Typically, we rely on screening and diagnostic tests that are available in medical facilities to provide us information regarding the disease status of our patients. However, it is important to assess the quality of these tests in order to make reasonable decisions regarding their interpretation and use in clinical decision-making (1) . In evaluating the quality of diagnostic and screening tests, it is important to consider the validity (i.e. sensitivity and specificity) as well as the predictive value (i.e. positive and negative predictive values) of the test.

Sensitivity is the probability (Pr) that a person will test positive (T+) given that they have the disease (D+). Specificity is the probability (Pr) that a person will test negative (T−) given that they do not have the disease (D−). These are conditional probabilities. The result in question is the accuracy of the test, and the condition is the true, yet unknown, presence or absence of the disease. Sensitivity and specificity are properties of the screening test, and, like physical properties, follow the test wherever it is used. They can be useful in determining the clinical utility of the test (as a screening tool vs. a diagnostic tool) as well as comparing new tests to existing tests. They are written mathematically as:

It is more common for sensitivity and specificity to be expressed from a 2×2 contingency table (Table 6) as follows:

Table 6: 2×2 Contigency Table – Test Characteristics

These parameters quantify the validity of a test when it is evaluated in a population that represents the spectrum of patients in whom it would be logical and clinically useful to use the test. The most obvious limitation of evaluating a screening test is identifying an optimal gold standard to determine the disease status. In the evaluation of new screening tests, existing tests are often used as the gold standard. Disagreement or poor sensitivity and specificity of the new test could mean that the new test does not work as well as, or that it is actually superior to, the existing test. A histological test from a biopsy is the least disputable gold standard. Nonetheless, the limitation with regards to the gold standard is unavoidable and must be recognized in the continuous evaluation of clinical screening and diagnostic testing.

In a study to evaluate bedside echocardiography by emergency physicians to detect pericardial effusion, 478 eligible patients were evaluated for the condition both by the emergency department physician and by the cardiologist (who had the clinical responsibility to make the diagnosis); the cardiologist's finding was used as the gold standard (21) .

An excerpt of the results is shown in Table 7 . From the data presented in the table, the following can be calculated:

Table 7: 2×2 Contingency Table for Pericardial Effusion Study

Since the sensitivity and specificity are like physical properties of the test, we can determine the portion of TP and TN regardless of the prevalence of the disease in the population studied. For example, if we had recruited 100 patients with pericardial effusion and 100 matching participants without pericardial effusion, the resulting table would yield the same rates of TP, TN, and identical values for sensitivity and specificity (Table 8) .

Table 8: 2×2 Contingency Table for Pericardial Effusion Example

Positive predictive value (PPV) is the probability (Pr) that the disease is truly present (D+) given that the test result is positive (T+). Negative predictive value (NPV) is the probability that the disease is truly absent (D−) given that the test result is negative (T−). Generally speaking, patients (and their physicians) are more concerned with these probabilities. These are also conditional probabilities. These parameters are written mathematically as:

PPV = Pr(D + |T + ), and NPV = Pr(D − |T − ). As with sensitivity and specificity, it may be more common to see the algebraic expressions:

The question is whether or not the individual patient's test result is true. Unlike sensitivity and specificity, PPV and NPV are dependent upon the prevalence of the disease in the population. The example below further illustrates this point.

Figure 1 shows PPV and NPV over the entire range of possible prevalence with sensitivity and specificity fixed at the values in the illustration. PPV and NPV are used to make clinical decisions concerning an individual patient based on the population from which the patient comes. As prevalence increases, PPV increases and NPV decreases. Thus, in populations where disease prevalence is high, there will be greater confidence that a positive test result is a true positive, and increased suspicion that a negative test result is a false negative. The reverse is true in populations where the disease prevalence is low (e.g. rare disease).

Figure 1: Effect of Prevalence on Positive and Negative Predictive Values

Diagnostic and screening tests, and their related sensitivities, specificities, PPVs and NPVs, facilitate the clinician's classification of a patient with regard to a specific disease status. The ultimate goal of the diagnostic process is to establish a diagnosis with sufficient confidence to justify treatment or to exclude a diagnosis with sufficient confidence to justify non-treatment (22) . In the process of determining a diagnosis (or not), the test results for a given disease should be kept within the context of the probability of disease prior to receiving results. Bayesian logic is the understanding of conditional probability which is expressed mathematically in Baye's Theorem. The theorem “indicates that the result of any diagnostic test alters the probability of disease in the individual patient because each successive test result reclassifies the population from which the individual comes” (22) .

Common Measures of Association and Statistical Tests

Measures of association are summary statistics that estimate the risk of an outcome or disease for a given exposure between two groups. Two frequently reported measures are the odds ratio and the relative risk. The odds ratio (OR) is calculated from a case-control study where the participants were selected by their outcome and then studied to determine exposure. Because the participants are selected on outcome, the case-control study reveals the prevalence of exposure among cases and controls. In case-control studies we calculate odds ratios because it is often a good estimate of the relative risk. Odds are the probability of an event occurring divided by the probability of the event not occurring. The OR ratio compares the odds of being exposed given a participant is a case ( Table 9 : a / a+c / c / a+c = a/c) relative to the odds of control participants being exposed (b/b+d / d / b+d = b/d). Using algebra to re-arrange the formula, the OR can be calculated as (Table 9) :

Table 9: Cell Naming Scheme for Doing Calculations from a 2 × 2 Table

Table 10: Diagram of Observed Frequencies Extracted for Odds Ratio Example

The relative risk or risk ratio (RR) is calculated from a cohort study where exposed and non-exposed participants are followed over time and the incidence of disease is observed. Because the hallmark of a cohort study is following a population over time to identify incident cases of disease, the cohort is screened to assure that no participant enrolled in the study has already experienced the outcome or disease event. Then, the cohort is followed for a specific period of time, and the incidence of events for the exposed and unexposed groups is measured. The relative risk can also be used to analyze clinical trial data. The relative risk (RR) is calculated from the labeled 2×2 table (Table 9) using the formula:

Table 11: Observed Frequencies Extracted from Relative Risk Example*

Both the OR and RR have confidence intervals (CI) as a measure of uncertainty. The method is similar to the one used for the binomial probability distribution. If a 95% CI excludes the value one (1) , then the ratio is significant at the level of p<0.05. A test of independence, as a category of methods, tests the hypothesis that the proportion of an outcome is independent of the grouping category. The alternate hypothesis, the conclusion made when the p-value is significant (p<0.05), is that the disease or outcome is more common among the exposed or unexposed group.

Chi-square tests are used to determine the degree of belief that an observed frequency table could have occurred randomly by comparing it to an expected frequency table. The expected frequency table is derived based on the assumption that the row and column totals are true as observed and fixed. The most commonly used chi-square test is the Pearson's chi-square test. This is used to analyze a frequency table with two rows and two columns. When the table is not symmetrical or is of dimensions other than 2-by-2, the method is still valid, and when used is called the Cochran's chi-square test. At the very least, the largest observed difference is significant if the table is significant. If the overall table is significant, this global significance can allow stratified sub-analyses of the individual comparisons of interest. It can also be helpful to look at the contribution to the chi-square test statistic by each cell and conclude that the largest of these cells are where the observed frequencies most deviated from the expected frequencies.

Table 12: Components for Calculating a 95% Confidence Interval Around Measures of Association

Tests of proportional disagreement are for paired data, either repeated measures in the same participants or participants matched on demographic factors then given different exposures and followed to compare outcomes. The best known of the tests of proportional disagreement is the McNemar's chi-square test. The outcome of paired data falls into four observations (++, −–, +–, −+). McNemar's test focuses on the discordant cells (+–, −+) and tests the hypothesis that the disagreement is proportional between the two groups. If when the outcome disagrees, the disagreement is more frequently −+ than +–, then we know that more pairs are improving or having better outcomes. A relatively new application of tests for paired data is the Combined Quality Improvement Ratio (CQuIR), which uses the McNemar's chi-square test as the basis, but combines participants with repeated measures and case-control matched pairs into one large database of analyzable pairs. This process maximizes the statistical power available from the population ( 25 , 26 ). Additionally, the ratio of discordant pairs (−+/+−) shows whether or not the disagreement is more often toward improvement. Included in the tests of disproportion is the Kappa statistic of agreement. The Kappa statistic evaluates the concordant cells (++ and −−) to conclude whether or not the agreement has enough momentum to be reproducible.

Thus far, we have used examples for analyses from observational studies. Experimental studies or clinical trials are analyzed in much the same manner. In clinical trials, patients are followed until some outcome is observed in the planned study period; these are incidence studies. As incidence studies, the RR will be the measure of association tested for statistical significance. Additionally, many clinical trials lend themselves to straightforward analyses with chi-square tests, ANOVA, or other methods that result only in a p-value. Table 13 summarizes common methods used to analyze healthcare data.

Measures of Association and Statistical Tests Commonly Used to Analyze Healthcare Data

For one example, we review the results of a trial of the beta-blocker, bucindolol, used in patients with advanced chronic heart failure (CHF) (27) . While it is accepted that beta-blockers reduce morbidity and mortality in patients with mild to moderate CHF, these investigators enrolled 2708 patients designated as New York Heart Association (NYHA) class III or IV to test the efficacy of the beta-blocker in reducing morbidity and mortality in patients with high baseline severity. The primary outcome of interest was all-cause-mortality, which, being a relatively rare event, drove the sample size requirement to 2800 in order to statistically detect a clinically significant difference of 25%. Once enrolled, patients were randomly assigned to receive either placebo or the beta-blocker, and neither the patient nor the physician knew to which treatment the patient was assigned. This study was stopped after the seventh interim analysis due to the accruing evidence of the usefulness of beta-blockers for CHF patients from other studies. At the time the study was stopped, there was no difference in mortality between the two groups (33% in the placebo group vs. 30% in the beta-blocker group, p=0.16). After the follow-up data was completed, adjustments for varying follow-up time could be made. The adjusted difference in mortality rate was still not significant (p=0.13). However, a sub-analysis of the secondary endpoint of cardiac death did yield a significant hazard ratio (HR) of 0.86 with a 95% CI of 0.74 to 0.99. This HR being less than the value 1 means that the beta-blocker was protective against cardiac death in the follow-up period. The CI not including the value 1 leads to the conclusion that this HR is statistically significant at the level of p<0.05. This secondary analysis is consistent with the decision of the study group to stop the trial early.

This concludes Part I of the series. In the next issue of The Ochsner Journal , we will present Part II which includes discussion of the significance of the study results, relevance of the results in clinical practice, and study limitations.

Table 14: Classification of Random Error

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Statistical Science

MPhil/PhD Statistical Science

The Statistical Science research programme at UCL aims to develop research students who can eventually make original contributions to the subject. Students are initially registered for the MPhil degree. No sooner than one year, they are transferred to the PhD degree with retrospective effect if they show a capacity for original work. The typical length of the PhD programme is three years for full-time students and five years for part-time students; an MPhil might be achievable in less.

• MPhil/PhD Statistical Science prospectus entry
• Research student profiles

The admissions process for the MPhil/PhD in Statistical Science operates on a rolling basis, with no fixed deadline for applications. Candidates should apply at least two months in advance of their intended start date.

The MPhil/PhD is accessible to students with, or expecting to achieve, a minimum of an upper second-class UK Bachelor’s degree, or a UK Master’s degree in statistics, mathematics, computer science or a related quantitative discipline. Overseas qualifications of an equivalent standard are also acceptable.

• Academic equivalencies

In addition to the academic requirements above, all students whose first language is not English must be able to provide recent evidence that their spoken and written command of the English language is adequate. For the MPhil/PhD in Statistical Science, applicants much reach at least the UCL standard level. Further information on this requirement is available at the link below.

• English Language qualifications accepted by UCL for graduate study

In applying for admission to the MPhil/PhD programme, candidates are expected to prepare an outline proposal of their work. This is crucial in identifying potential supervisors. Thus, candidates should peruse the research interests of staff before applying. A list of staff members currently accepting applications for PhD supervision is given below, including an indication of their current research interests and a link to their personal webpage.

It may be helpful to contact a potential supervisor before submitting a formal application. For more information on how to contact potential supervisors and write a research proposal please see UCL's guidance document . Applications on which no potential supervisor has been specified will still receive consideration, however, in such cases it would be especially important to demonstrate in your reasons for applying that your academic interests align with the Department's active research areas .

Unlike the taught Statistics MSc programme, the MPhil/PhD has no required curriculum. However, students are expected to agree on a customised programme of study with their supervisor, which may involve specialisation courses (either at UCL or at the London Taught Course Centre) or independent reading. Attendance at research seminars is encouraged, and students who have been upgraded to PhD status are required to present their research in a separate seminar stream once per year. Finally, the UCL Graduate School has its own requirements for training courses.

• London Taught Course Centre
• UCL Graduate School Training Requirements

Some departmental funding is usually available. UCL also offers a number of scholarships and other funding for UK, EU and overseas students undertaking research studies at the University. Further information, including eligibility criteria and application deadlines, can be found at the links below.

• Research studentships
• Funding for students on postgraduate research courses

For more information on the programme please contact:

Ms Marina Lewis stats.pgr-admissions AT ucl.ac.uk +44 (0)20 7679 1868

Please note that all professional services staff are currently working away from the office and are therefore unable to take phone calls on the number above.

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HDR UK - Turing Wellcome PhD application 2024

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Job Description

The HDR UK-Turing Wellcome PhD Programme provides unrivalled opportunities for people from a variety of academic and professional backgrounds who are inspired to become future leaders in health data science.

Its underlying philosophy is that health data science requires a combination of expertise spanning three fundamental areas: statistical, computational and health sciences.

Our programme offers:

Enhanced and tax-free stipends with increases every year (Y1: £23,955).

Fully-paid tuition fees at the UK 'Home' rate

Research expenses and travel costs

Bespoke training delivered with our industry and academic partners with a cohort of students

A bursary to support the next step of your career post-PhD

Entry requirements:

All applications will be reviewed on a rolling basis, so don't hesitate to apply!

If you are considering applying to us you must have (or be on track to obtain):

A first class or 2:1 undergraduate degree in statistics, mathematics, computer science, physics or an allied subject* or

Any undergraduate degree subject and outcome but can demonstrate your suitability for this programme through additional qualifications or research experience.

*Allied subjects can include: engineering, machine learning, data science, robotics, systems biology, bioinformatics/biostatistics, epidemiology.

Applicants for previous rounds need not reapply.

Further information:

https://www.hdruk.ac.uk/study-and-train/study/phd/hdr-uk-turing-wellcome-phd-programme-in-health-data-science/

Privacy policy:

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The versatility of statistics and data science across majors

Statistician Dennis Sun is on a mission to help students see how statistics, data science, and probability apply to their everyday lives by infusing the introductory college courses in these subjects with relatable examples; getting data into the hands of students early; and making his new online statistics textbook freely available—and accessible—for everyone.

If you poked your head into the classroom of Stanford statistician  Dennis Sun , you might be surprised by what you see. On one day, you might observe students tossing a beach ball-sized inflatable globe back and forth. On another day, you might see students circling their birth dates on a calendar, performing a Coke versus Pepsi taste test, or counting the clicks of a Geiger counter as it detects radiation. 

These activities may seem outlandish, but they are tangible examples of ways we can measure uncertainty and variability—two key concepts in the fields of statistics, data science, and probability.

Creating courses that inspire students to dream up unconventional, useful, and (frequently) fun ways to apply these disciplines to their academic interests and daily lives drives the work of Sun, an associate professor (teaching) of statistics in the  Stanford School of Humanities and Sciences and a data scientist at Google. 

Sun earned his doctorate in statistics at Stanford in 2015 and joined Stanford’s faculty in 2023. Since then, he has been working to demystify these subjects and help students see that these topics have many practical applications.

“I’ve always been interested in applying statistics to topics you don't traditionally think of,” Sun said. “When I came to Stanford as a graduate student in statistics, I took classes in the music department.” 

His subsequent research applied audio signal processing methodology to music, such as the problem of separating out the different instruments in a recording. Building on this research, Sun then applied these methods to speech recognition in noisy recordings.

Blending music with statistics may seem like a disparate mashup but statistics can be applied to just about anything, Sun explained. Unfortunately, statistics and data science aren’t being applied as widely as they could be. One possible reason is that people tend to view these topics as tools for mathematicians and statisticians, and as a result they aren’t always taught in a way that is accessible to everyone.

“Starting with formulas and theorems may work well for students who want to be professional statisticians, but that may not be the best way to connect with all the students who will need statistics in their lives,” Sun said.  

This is especially true for students who are exploring career paths and have yet to discover how statistics, probability, and data science could be relevant to their academic interests.

Students interested in pursuing majors in economics, engineering, or biology may not know that data science can be used to, for example, predict house prices and online shopping behavior, identify the variables that affect fuel efficiency for different models of cars, and reveal the likely location of an endangered animals’ territory so conservation efforts can focus there.

“Concrete examples that people can connect with help make statistical concepts more accessible,” Sun said. “We live in a world where uncertainty is inevitable. Statistics is fundamentally about quantifying that uncertainty and using that information to help us make decisions.”

Piquing the interest of students early

Sun will become director of the Program in Data Science this fall. It is perhaps fitting that he took on the challenge of rethinking the way statistics, data science, and probability are being taught at the introductory college level, because his own interest in statistics was profoundly influenced early in his college career.

“In college I took a very inspirational probability class from a professor who I still consider a mentor today—Joe Blitzstein, professor of statistics at Harvard,” Sun said. “I was a math and music major, but that class led me to take more statistics classes. When I graduated, statistics was what I wanted to do.”

Sun is currently reimagining three essential introductory courses related to data science, statistics, and probability that students take as undergrad­uates— Principles of Data Science  (DATASCI 112), Introduction to Statistics for Engineers and Scientists  (STATS 110), and Introduction to Probability  (STATS 117) .

“Many students are required to take an introductory statistics class for their major, and many of them are not looking forward to that class,” Sun said. “I'm interested in showing them how statistics can be relevant to their lives, their majors, and their careers.”

To achieve this, Sun pairs relatable scenarios with everyday objects to create memorable learning experiences for his students. One example he recently used in his class involves tossing a large inflatable globe around the classroom.

Sun pulled a beach ball-sized inflatable globe out from behind his desk, tossed it up in the air, and caught it, making a satisfying slap .

“To illustrate a certain idea in statistics called a confidence interval, I pose this question to the class: ‘What percentage of Earth is covered by water, and how could we estimate that?’” Sun said. “Eventually, a student will suggest tossing the inflatable globe around the room. Whenever a person catches it, you see if their finger lands on water or land.”

The students throw the inflatable globe around the room from person to person, tallying the number of times their right index finger lands on water. If, for example, that number is 13 out of 20 times, the corresponding estimate is that 65% of Earth is covered by water. 

“The students quickly realize there's some uncertainty associated with that number,” Sun said. “Because if we toss the globe around the class another 20 times, the result might be 15 out of 20, or maybe 12 out of 20. The experience really illustrates the idea of variability and uncertainty, which are central to statistics.”

Getting data into the hands of students early

In addition to his efforts to improve the way that introductory statistics, data science, and probability are being taught, Sun is using two other approaches to make these topics more accessible. The first is getting data into the hands of students early.  

Traditionally, students must take several courses in math and probability before they get the opportunity to work with data.

“Working with actual data is what gives students a sense of what data science is all about,” Sun said. “So I created a first-year data science class that students can take their second quarter at Stanford, even if they haven’t taken math courses yet.” 

Sun hopes that giving students the option to work with data during their first year of college will be a turning point for them as they weigh different academic paths. 

“I’m also working to serve the broader community as well,” Sun said. “How can we, as a society, develop more statistical literacy and help reach more people?”

Sun and two Stanford colleagues in statistics, lecturer Gene Kim , and doctoral scholar Anav Sood , just published a free textbook, The Art of Chance: A Beginner's Guide to Probability , that is available online for anyone who wants to learn more about the topic in a reader-friendly and approachable way. 

People are increasingly seeing the value of statistics and data science, Sun explained. But there is still much that can be done to help students get a solid grasp of the fundamentals of these subjects and—hopefully—inspire them to keep studying and learning about these topics in the future. 

“When I started as a doctoral student at Stanford, if I told someone I was a statistician, I’d get comments like, ‘statistics, oh I hated that course,’” Sun said. “I think that’s changing. More often you hear, ‘I love my stats course.’”

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Grant Rotskoff wins 2024 Scialog Collaborative Innovation Award

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© Stanford University, Stanford, California 94305

Textbook of Medical Statistics

For Medical Students

• © 2024
• Xiuhua Guo 0 ,
• Fuzhong Xue 1

School of public health, Capital Medical University, Beijing, China

You can also search for this editor in PubMed   Google Scholar

School of Public Health, Shandong University, Jinan, China

• Combines statistical methods with the common manipulation of SPSS software
• Highlights learning objectives and key concepts in each chapter
• An ideal textbook for MBBS (Bachelor of Medicine and Bachelor of Surgery) student

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About this book

This book introduces basic concepts, principle, and methods of medical statistics systematically and practically, especially in the statistical design of the experiment in terms of the specific problems, adequate use of statistical methods based on actual data and the reasonable explanation for statistical results.

This textbook combines statistical methods with the common application of SPSS software, which is flexible, convenient, and user-friendly; thus, students can focus on the deep understanding of statistics.

The authors emphasize the application and generalization of statistical methods, and combine these methods with the modern statistical theory, such as sequential contingency table and multivariate statistical modelling, etc.

This book is a useful textbook for graduate and undergraduate students in medical schools, including MBBS (Bachelor of Medicine and Bachelor of Surgery) student.

• confidence intervals
• statistical methods
• study design

Table of contents (15 chapters)

Front matter, introduction to medical statistics.

• Fuzhong Xue, Tong Wang, Shicheng Yu

Study Design, Sample Size Estimation, and Selection of Statistical Method

• Xinghua Yang, Junhui Zhang

Statistical Tables and Graphs

• Zhihang Peng, Yuxue Bi

Descriptive Statistics of Continuous Variables

• Lijuan Wu, Chanjuan Zhao

Description of Categorical Variables

• Dongliang He

Inferential Statistics: Confidence Interval

• Ya Fang, Ying Hu

Inferential Statistics: t- Tests

• Qi Gao, Suling Zhu

Analysis of Variance

• Yupeng Wang, Qiuju Zhang, Meina Liu

Chi-Square Test

• Yanxia Luo, Hongbo Liu

Nonparametric Tests

• Hong He, Linlin Li

Correlation and Simple Linear Regression

• Liqin Wang, Ying Guan, Xia Li

Multiple Linear Regression Analysis

• Xiuhua Guo, Xiangtong Liu, Guirong Song

Logistic Regression

• Wenli Lu, Yuan Wang

Survival Analysis

• Hongmei Yu, Yan Guo

Cox Regression

• Mingqin Cao

Editors and Affiliations

Fuzhong Xue

About the editors

Editor Xiuhua Guo is a doctor and professor of Epidemiology and Health Statistics, Capital Medical University, Beijing, China. The main research areas focus on the hierarchical data model or data mining model to handle big data of high dimensional environmental and genetic factors in major infectious diseases, cardiovascular diseases and metabolic disorders, thus to provide scientific proof for the causes of diseases and intervention strategies.

Bibliographic Information

Book Title : Textbook of Medical Statistics

Book Subtitle : For Medical Students

Editors : Xiuhua Guo, Fuzhong Xue

DOI : https://doi.org/10.1007/978-981-99-7390-3

Publisher : Springer Singapore

eBook Packages : Medicine , Medicine (R0)

Copyright Information : Zhengzhou University Press 2024

Softcover ISBN : 978-981-99-7389-7 Published: 20 June 2024

eBook ISBN : 978-981-99-7390-3 Published: 19 June 2024

Edition Number : 1

Number of Pages : XII, 220

Number of Illustrations : 62 b/w illustrations, 24 illustrations in colour

Topics : Epidemiology , Applied Statistics , Biostatistics

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Prestigious College Grads Have Better Health Later In Life, New Study Finds—Here’s Why

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Attending a private U.K. high school or a university with higher status is tied to better cognitive function, heart health and BMI decades after graduation, according to a new study published Tuesday, and researchers believe more disposable income and physical activity advantages may play a role.

A woman on graduation day.

Around 7% of the study’s participants (all of whom were in the U.K.) attended a private high school, less than 4% attended a grammar school—what the researchers consider “selective without fees”—and 89% of participants went to a state funded school, while 7% attended a higher status university, which are highly regarded Russell Group schools like the University of Oxford and University of Cambridge.

The researchers found those who attended a private high school had better cardiometabolic and cognitive outcomes than those who went to state funded schools, and limited evidence suggests the private school group also had lower BMIs and better blood pressures, according to a paper published in the Journal of Epidemiology & Community Health.

There were no major health differences found between the private school group and the grammar school group except for BMI, where private school attendance was associated with a lower BMI.

Higher-status university attendance was associated with lower BMI and better cognitive performance compared to participants who attended “normal-status” universities; having no degree was associated with the worst health outcomes, except for better grip strength and balance.

The researchers believe a couple reasons may explain the study’s results: Students who attend private high schools typically have greater physical activity than their peers, and participants who attended higher-status schools had more disposable income, so they could focus on their health more.

The study included over 8,500 participants between the ages of 46 and 48, who were a part of the 1970 British Cohort Study followed by researchers since they were born in 1970, and were selected and interviewed about their mid-life health between 2016 and 2018.

A previous study done on the British cohort found those who attended private high schools and high-status universities had lower BMI and better self-reported health scores than their counterparts.

Key Background

Similar results are visible in the United States: Attending U.S. colleges with selective admission rates was associated with slightly higher cognitive performance later in life, a Research on Aging study found. Adolescents who attend U.S. schools with advantages like smaller student-to-teacher ratios and teachers with better salaries have better cognitive skills between the ages of 65 and 72 than those who attend schools with less or no advantages, according to a 2020 study . Higher economic status, social position and more access to “highly-resourced” elementary and middle schools are all reasons the researchers found for these health outcomes. Researchers also discovered higher education helps people find higher paying jobs with less safety hazards, so this may factor into the better health outcomes.

Receiving any type of education after high school has also been linked to better health outcomes worldwide. The higher a person's level of education, the lower their risk of premature death, according to a January global study published in the Lancet. A person's risk of death dropped by an average of 2% with every additional year of education they attained, the study found.

Surprising Fact

Education level has also been linked to specific health conditions. People whose highest level of education was middle or elementary school had a 52% higher risk of dying from coronary heart disease than those who attended graduate school, according to a 2019 study . Diabetes risk may also decrease with more education: 13.1% of adults in the U.S. who have diabetes have less than a high school education, compared to 9.1% who graduated high school and 6.9% with more than a high school education. Black and white people with 12 or less years of education have between a 60% and 180% higher cancer mortality rate versus those with 16 or more years of education, a study by the American Cancer Society found.

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June 21, 2024 | Ashley O'Connell and Nicole Dobrzanski, UConn School of Nursing

UConn School of Nursing Receives Award to Help Increase Nurses in the Workforce

Associate Dean Dr. Annette Jakubisin-Konicki, PhD, ANP-BC, FNP-BC, FAANP, FAAN receives award to provide a low-interest option for students.

UConn School of Nursing. (Peter Morenus/UConn Photo)

The School of Nursing has been awarded $763,308 through the Health Resources & Services Administration’s (HRSA) Nurse Faculty Loan Program (NFLP). Launched in 2004, this program seeks to increase the number of qualified nursing faculty through their low interest loans to help prepare and train qualified nurse educators to fill faculty vacancies and increase the number of nurses entering the workforce.

“A robust, geographically dispersed nurse faculty workforce is essential to producing the nursing workforce needed to meet US health care needs,” the NFLP states. The School of Nursing applies for NFLP funding each year to support nurses pursuing doctoral education who are interested in becoming nursing faculty.

NFLP- eligible students enrolled in either the DNP or PhD programs at UConn may apply for funding in Fall 2024. The NFLP funds cover tuition and other qualified cost for up to five years, with a maximum of $40,000 per year. Students awarded the NFLP funding may cancel 85% of their loan in return for serving four consecutive years as faculty in any accredited school of nursing or precepting advanced practice nursing students within an academic-practice partnership framework for four years.

The Project Director (PD) for this award is Dr. Annette Jakubišin-Konicki (PhD, ANP-BC, FNP-BC, FAANP, FAAN). Dr. Jakubišin-Konicki is the Associate Dean for Graduate Studies. The award administration is through the cooperative efforts of the UConn School of Nursing, Financial Aid and Bursar’s Office. The applications, available in fall 2024 will be on a first-come, first-served basis. Priority funding is given to those with prior NFLP funding, pursuing doctoral nursing education and meeting the eligibility requirements.

“The Nurse Faculty Loan Program (NFLP) is essential for expanding nursing training capacity. It supports nursing doctoral education, increasing the pool of qualified nursing faculty. By offering low-interest loans, it encourages doctorally prepared nurses to become effective faculty scholars and reduces the associated financial barriers through loan cancellation.”- Dr. Annette Jakubišin-Konicki

In receiving this award, UConn’s School of Nursing hopes to further expand the accessibility and the capabilities of its students. The opening of the application cycle for the upcoming academic year will occur in August.

For any further information, please contact Dr. Jakubišin-Konicki at [email protected].

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