biostatistics phd reddit

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Anyone with MD/PhD with PhD in biostatistics / statistics / mathematics?

• Thread starter cdpiano27
• Start date Feb 18, 2008

Senior Member

• Feb 18, 2008

I would agree with you. http://en.wikipedia.org/wiki/National_Institute_of_Biomedical_Imaging_and_Bioengineering Signed into law by Bill Clinton. How could you not like it ?  

Full Member

• Sep 21, 2011

pantouka said: I am a college pre-med sophomore interested in MAYBE going the MD/PHD route with an emphasis in biostatistics, but I'm having difficulty imagining exactly what the job would be like with the intersection of the two degrees. Click to expand...

pantouka said: 1) Would an MD/PHD-stats person have a huge role in running clinical trials? Would they ever be involved in analysis? Click to expand...

pantouka said: I2) How would the PHD portion of the joint degree be different from an MPH joint degree in practice? Click to expand...

pantouka said: I3) Are there any fields of medicine better suited for a stats PHD? I can honestly see relevance in disciplines ranging from psychiatry to infectious diseases. Click to expand...

I'm planning to pursue mathematics/statistics for my PhD and future career. There aren't many of us--maybe one or two graduate with it a year in the US... 1) Would an MD/PHD-stats person have a huge role in running clinical trials? Would they ever be involved in analysis? Yes, I'm planning to be involved in analyses and study design in clinical trials, as well as basic science research (hopefully genomics and population health). 2) How would the PHD portion of the joint degree be different from an MPH joint degree in practice? The MPH will not teach you enough statistics to do your own analyses very easily. Typically, MPH students only learn analysis up to multiple regression. The emphasis is public health, not statistics. In statistics/mathematics, you'll learn how to create new ways to analyse data that you can test in clinical trials and basic science research (probability theory, generalized linear models, network-based methodology...). An MS in statistics would probably give you a good overview, though. 3) Are there any fields of medicine better suited for a stats PHD? I can honestly see relevance in disciplines ranging from psychiatry to infectious diseases. Genetics, public health, and neuroscience are big areas in stats these days, so specialties relating to these areas of research might be a good starting point. I'm planning on a career in academics and government with medical service work abroad. A few things to consider in undergrad: -Most stats PhD programs require 3 semesters of calculus, linear algebra, several statistics courses (preferably with calculus), and a probability course. -You'll probably need the GRE in addition to the MCAT (not GRE math subject exam, though). GRE math should be substantially over 700. -There aren't a lot of schools offering this. U Minnesota, MUSC, U Miami, U Florida, UIC MSP, Stanford, and U of Chicago were the ones I found offering math/stats options. Bioinformatics is also a good option that's offered at more schools...  

what exactly is the difference between bioinformatics and biostatistics?  

• Sep 22, 2011

tortuga87 said: what exactly is the difference between bioinformatics and biostatistics? Click to expand...

Bioinformatics uses computer science to gather data in biology (genomics technology, algorithms to compute things for statisticians). Biostatistics is the application and development of new methods of data analysis from the field of mathematics (usually have someone in computer science to help you write the programs you need to analyze data by the new method).  

It seems like: biostatician + learning some programming > bioinformatician because you can develop more elegant methods with biostatistics?  

• Sep 23, 2011

tortuga87 said: It seems like: biostatician + learning some programming > bioinformatician because you can develop more elegant methods with biostatistics? Click to expand...

Biostaticians develop tools for analyzing data. Bioinformaticians develop tools for gathering data. Generally people in one will have some grounding in the other, but it's silly to say one is better than the other...  

I'm currently a 2nd year MSPH in Biostatistics who deferred medical school to complete my masters degree. So here are the things that I've learned that I think would be most helpful: 1. Courses The main difference between MSPH/MPH and PhD courses is that the masters degree will ultimately be in public health, and biostats is just a concentration. Therefore, as a masters student you will be required to take public health courses such as epidemiology, behavioral science and health education, health policy management, and environmental sciences. For PhD students, these courses are optional although most choose to take these anyways. In terms of biostat courses, masters and PhD students have the same coursework the first year, with the PhD student also having to take an additional class to prepare them to be teaching assistants in the following years. The second year, PhD students go on to take more advanced statistical and probability theory classes. They may also be required as part of their stipend package to TA introductory biostat classes. 2. Thesis The masters thesis takes about a semester and a half to complete, and is significantly less involved than a PhD dissertation. The masters thesis is mainly explanation and application of a statistical model to a data set, whereas a PhD dissertation is expected to introduce new statistical theory and/or methods to a very specific area of research. Starting from about the third year of the PhD program, the students start to devote the majority of their time to working on their research and holding outside jobs. 3. Jobs Overall, masters programs have more of an emphasis on application and practical experience, and PhD programs have more of an emphasis on theory. Jobs that prefer masters degrees to PhD degrees are the ones that need someone who is more involved in the entire research process and study design. This often requires the biostatistician to physically go into, for example, a hospital and monitor the accuracy of which data is being collected. PhD-level biostatisticians would be over-qualified to work in this type of role, so they serve mostly as the lead data analyst. Outside of academia, PhD degrees are more often hired by biostat consulting firms and pharmaceutical companies to handle complex data analysis. All in all, if you want to be involved in the actual study and not just the data analysis, masters degrees are probably the way to go. 4. MD/MPH or MD/PhD When you're a biostatistician analyzing data, its always helpful, if not necessary, to understand the context of the problem you're working with. If you start working on a study about degenerative lumbar spine diseases, it would be really helpful if you already have working knowledge about the spine going into the project. However, if you don't have that knowledge, you can always do the background research yourself, although it takes a lot more energy and work. In this case, having an MD would definitely be a leg up. Also, if you hold an additional MD degree, you are most likely the one that is asking the research questions rather than working on other people's trials. This is a HUGE advantage because understanding your research backwards and forwards means that you are able to look at your data from multiple perspectives. For example, as a clinician you may know that certain biological mechanisms can affect your outcome and you actually know the statistical methods that allow you to identify and analyze those variables. On the other hand, a stastician without such an in-depth background in medicine and/or biology would not be able to reach the same conclusions. 5. Future Many studies have pointed out that PhD programs don't place enough of an emphasis on application of theory. Case in point: I am currently working on a study that has a lot of missing data due to patients not coming in for follow-up. To perform an accurate analysis, I need to know how to account for this missing data. I go a PhD-level professor who gives me literature to read on some theories on how the missing data might affect my conclusions. I go to a masters-level professor who tells me exactly what to do with my data to get a more accurate conclusion. As the field evolves, biostatisticians need to have both a firm grasp of theory as well as how to apply it in real-life situations. I hope all this helps. As you can tell, I'm very passionate about biostats and I really do wish there were more people that are interested in this field. Whatever path you decide, even if you decide to just pursue a masters degree, there is such a demand for biostats that your future will be bright now matter what.  

• Sep 26, 2011

I personally am not getting a PhD in stats, but I did work for an MD/PhD when I was an undergrad who was a dermatologist whose research was skin cancer epidemiology. He was one of the archetypal 75/20/5 guys who spent most of his time doing research, two half days a week in general derm clinic, and taught a few lectures a semester. He was heavily involved in clinical trials and evaluating public health programs for skin cancer.  

prolixity29

• Feb 20, 2012

Bump. I have a few questions regarding pursuing an MD/PhD in biostatistics... Really look forward to hearing your responses. I am currently pursuing my MSPH in Biostatistics in the Philippines (though I am from the US). After finishing my degree, I will attend medical school in the US. Originally, I had decided to study biostatistics out-of-interest and because I received a scholarship for my glide year, but I am truly enjoying the theoretical coursework and am now considering studying biostatistics when I enroll in medical school. The applied problem areas also interest me deeply, particularly genomics and environmental health. Since I will already have a master's degree by the time I matriculate into med school, it seems that the next logical step would be to shoot for a PhD. The problem is though that I am a non-traditional student in my late 20's. I want to have a family and need to start making an income and this would prolong my non-income-generating years... My eventual career goal in getting an MD/PhD would be either working/researching biostatistics while practicing in an academic hospital or consulting for industry (e.g. pharma or biotech) while practicing. Questions: 1) Is pursuing an MD/PhD at my age impractical? 2) Can you do a fellowship in biostatistics that would get you to the same place as an MD/PhD? I have tried searching for "biostatistics fellowships" and the ones I have seen are for post-docs. 3) Assuming I did pursue a PhD, would I be able to bypass the master's level courses given my MSPH? If so and assuming that I work diligently and do not burn out, what is a reasonable estimate for the number of years I could shave off the dual degree? Could it be done in 6 years? Thanks so much for your responses.  

• Feb 21, 2012

prolixity29 said: Bump. I have a few questions regarding pursuing an MD/PhD in biostatistics... Really look forward to hearing your responses. I am currently pursuing my MSPH in Biostatistics in the Philippines (though I am from the US). After finishing my degree, I will attend medical school in the US. Originally, I had decided to study biostatistics out-of-interest and because I received a scholarship for my glide year, but I am truly enjoying the theoretical coursework and am now considering studying biostatistics when I enroll in medical school. The applied problem areas also interest me deeply, particularly genomics and environmental health. Since I will already have a master's degree by the time I matriculate into med school, it seems that the next logical step would be to shoot for a PhD. The problem is though that I am a non-traditional student in my late 20's. I want to have a family and need to start making an income and this would prolong my non-income-generating years... My eventual career goal in getting an MD/PhD would be either working/researching biostatistics while practicing in an academic hospital or consulting for industry (e.g. pharma or biotech) while practicing. Questions: 1) Is pursuing an MD/PhD at my age impractical? 2) Can you do a fellowship in biostatistics that would get you to the same place as an MD/PhD? I have tried searching for "biostatistics fellowships" and the ones I have seen are for post-docs. 3) Assuming I did pursue a PhD, would I be able to bypass the master's level courses given my MSPH? If so and assuming that I work diligently and do not burn out, what is a reasonable estimate for the number of years I could shave off the dual degree? Could it be done in 6 years? Thanks so much for your responses. Click to expand...

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Should I pursue a PhD in Statistics? [closed]

I am trying to decide whether or not I should take a PhD program in Statistics. I am not inclined to get a position at a university after my PhD, my goal is rather to get hired by Contract Research Organizations involved in Clinical Trials, either as a Biostatistician or Statistical Programmer. At the moment, I like working with SAS so I am more inclined to become a Statistical Programmer.

Would the Clinical Trials companies hire a PhD holder for their Statistical Programmer position? From what I see on the job postings, companies typically asks for Master's degree in Statistics (or sometimes even just a Bachelor's degree) for Statistical Programmer positions, but I just wanted to hear opinions of people who actually works in Clinical Trials for private companies.

• biostatistics
• clinical-trials

• 1 $\begingroup$ I worked for some pharmaceutical companies and a CRO during my career. In my experience the statistical programmers did not have PhDs. $\endgroup$ –  Michael R. Chernick Commented Mar 22, 2018 at 0:51
• $\begingroup$ Unless it's PhD in UK or some other country where it can realistically be done in 3 years, I'm not sure it's worth the opportunity cost $\endgroup$ –  Aksakal Commented Mar 22, 2018 at 0:52
• $\begingroup$ Thank you for your comments! I have a follow up question -- if I ever choose to become a Biostatistician rather than Statistical Programmer, would it be more advisable to take a PhD program in Stat? Would CROs consider new PhD graduates to be just as qualified as someone with Master's degree with years of experience, when it comes to Biostatistician positions? $\endgroup$ –  jschnieder Commented Mar 22, 2018 at 1:02
• $\begingroup$ My impression is that something beyond a Bachelor is usually expected Biostatistician roles, but beyond that the technical strength / background / degree / interests etc. will mostly affect what one gets to work on. It may also affect hiring/interview decisions (and attitudes vary - some pharmaceutical companies may occasionally go through "we only hire PhDs"-phases (even of that is not truly needed for most of three work), I have the impression that this is a lot less common at CROs. $\endgroup$ –  Björn Commented Mar 22, 2018 at 6:31

I have a PhD in statistics, where I specialised in Bayesian theory. These days I am doing work as lead statistician on RCT research in a couple of health projects. This involves RCT planning and execution, analysis of data, and reporting outcomes of trials. On the basis of my experience, this is what I think:

For the vast majority of work doing statistical programming in health, a Masters-level education in statistics would be sufficient. The main skills you will need for practice are good skills in using statistical computing (e.g., SAS, R, etc.) to organise, clean, and analyse data, and create routines for automated reproducible analysis. Most of the models you use are likely to be commonly used model forms that have already been programmed into statistical software (e.g., regression, GLMs, GLMMs), and it is rare that you need custom models. You should put in the time to understand these basic model categories deeply, and learn the implementation of these models in statistical programming.

A PhD is likely give you a deeper knowledge of theory than most others in your field who lack that background, and better mathematical skills. It also gives you practice at the process of research leading to peer-reviewed published work. This level of study gives you very solid "first principles" knowledge of statistical theory and models, which gives you an advantage when you encounter problems requiring some variation of standard models or custom models.

Aside from general theory knowledge, and improved mathematical ability, the value of a PhD depends a great deal on the relevance of your research topic to your future field. If you undertake a research project in the field of statistical programming for RCTs, that will be very helpful for a future career in that field. If your topic is not relevant to your future field (as in my case) the value you will obtain will just be a general improvement in your theory and mathematical abilities, and broader knowledge of statistics.

Even with an irrelevant project, a PhD in statistics is going to give you some training that is useful in a general sense (better theory knowledge, better maths, etc.). Although there is value in this program, there is also a big opportunity cost . If you spend a standard full-time period of four years doing a PhD, that is going to be at the expense of four less years of professional experience in the industry. Using the equivalent amount of time practicing in the field is likely to give you much more skill in the day-to-day operations in that field than undertaking a PhD.

If you're even moderately undecided about a PhD, I suggest you don't do it. A PhD candidature is a major commitment requiring a hard slog through a lot of road-blocks. It is rarely smooth, and the academic landscape is littered with the bodies of PhD drop-outs. For what you want to do, I would suggest trying to get industry experience as early as possible, and then consider later postgraduate education once you have a bit of experience.

Not the answer you're looking for? Browse other questions tagged biostatistics clinical-trials careers phd or ask your own question .

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Doctor of Philosophy in Biostatistics

UCLA Fielding School of Public Health

The PhD in Biostatistics trains biostatisticians to understand and confidently solve difficult scientific data analysis problems in the health sciences from problem conception, through data collection, to choosing the appropriate data analyses, and reporting of results. Rigorous courses are taught by world leaders in their specialties and train students in mathematical statistics, advanced biostatistical methods, big data, machine learning, data science, and statistical computing. The PhD trains statisticians who can develop and apply appropriate statistical methods to solve novel problems in the health field and who can conduct statistical methodological research.

Mathematics preparation for the program should include at least two years of calculus, as well as some linear algebra:

• Mathematics 31A, B - Calculus and Analytic Geometry
• Mathematics 32A, B - Calculus of Several Variables
• Mathematics 33A, B - Matrices, Differential Equations, Infinite Series
• Mathematics 115A - Linear Algebra

More math is always better, particularly for the PhD program, where real analysis (UCLA Math 131A, 131B), and linear algebra (UCLA Math 115A, 115B) are desirable.

The program requires the completion of the following elements:

1. Course Requirements

Unless previously taken, students are required to take the following courses:

• Biostatistics 200 A, B, C: Method in Biostatistics
• Biostatistics 202 A, B: Mathematical Statistics
• Biostatistics 216: Mathematical Methods for Biostatistics
• Biostatistics 250 A, B: Linear Models
• Biostatistics 250 C: Multivariate Biostatistics
• Biostatistics 257: Computational Methods for Biostatistical Research
• Biostatistics 245 & 246: Doctoral Seminar
• Biostatistics 409: Biostatistics Consulting
• Mathematics 131 A: Real Analysis (must be taken in year 1 by students with limited or no prior experience to Real Analysis)
• One 4-unit course in the Department of Epidemiology (either EPI 100 or 200A)
• One 4-unit course in board public health (PH 150/C201 or HPM M242)
• Minimum of 6 4-unit Biostatistics special topics courses from Biostatistics 202C, 210 and above.

2.  Written Examinations

Students must pass 2 written examinations, the PhD preliminary exam and the PhD written advanced qualifying exam.

Failure to secure a passing grade in a maximum of two attempts of the PhD preliminary exam and the PhD written advanced qualifying exam will result in the department recommending the student to the graduate division for academic disqualification.

PhD Preliminary Exam

This exam is offered in September just before fall classes begin. Students typically take this exam at the beginning of their second year of study after completing the related coursework and are expected to pass at a level that predicts successful completion of the PhD program. The exam covers material in the following courses: Biostatistics 200 A, B, and C, and Biostatistics 202 A and B.

• Students must pass the exam at a level expected of doctoral students
• Students have a maximum of two attempts to pass the exam

PhD Written Qualifying Exam

This exam is offered in September just before fall classes begin.The scope of the exam includes material from the following courses: Biostatistics 250 A, B, and C. Students typically take the exam after completing the necessary coursework in the beginning of their third or fourth year of graduate study.

3. Oral Qualifying Exam

The oral qualifying exam evaluates a student’s understanding of statistical theory and ability to apply the theory, and reviews the proposed dissertation topic. The student should prepare a written dissertation proposal that includes background, preliminary work, and a research plan for completing the work. While there are no absolute page requirements, proposals are generally between 15 to 50 pages, with additional pages for figures and references. The proposal should be distributed to members of the dissertation committee in advance of the exam. The proposal is expected to be delivered to committee members at least two weeks before the scheduled oral exam. If the student expects the proposal to be delivered less than two weeks before the exam, the student should obtain advance approval from each committee member. During the oral exam, the student will present and defend the proposed work. The student can expect the majority of the questions to pertain to the proposal, however additional questions may be asked to assess general understanding of biostatistical principles. The overall objective of the exam is to evaluate whether the student has the ability and adequate plans for conducting PhD dissertation research.

4. PhD Dissertation and Oral Defense

The PhD dissertation is original research that advances the field of biostatistics. The dissertation is completed under the guidance of a Department of Biostatistics faculty member who serves as the adviser. Examples of dissertations from previous graduates are available in the Biostatistics Library. After successfully completing a dissertation, an oral examination defending the dissertation is conducted by the dissertation committee. A failed examination may be repeated once on the recommendation of the committee.

The PhD in biostatistics is typically a four-year program following the MS, although some students may complete the program in less time.

The sequence of classes taken during the first year of study depends on the student’s background. Doctoral students establish a sequence of courses in consultation with their academic adviser to best prepare them for the comprehensive exams. 

Graduates from UCLA Fielding's Department of Biostatistics obtain employment as faculty members at universities and as leaders in government research organizations, and pharmaceutical and biotechnology companies locally within California, throughout the United States and across the globe. 

Examples of positions held by graduates include: 

• Tenure-Track Faculty
• Biostatistician
• Statistician
• Research Scientist
• Pharmaceutical/Biotechnology Biostatistician
• Data Analyst
• Data Scientist

View a list of faculty in the Department of Biostatistics.

For the most up to date fees and more information on fee breakdown, visit the  registrar's office .

Please see the cost and aid section of our website for information on awards, scholarships, training opportunities, employment, summer internship funding, and need-based aid. Please note that opportunities listed under 'Summer Internship Funding' are only applicable to MPH students.

Desired Qualifications

In addition to the University’s minimum requirements , competitive applicants are expected to possess strong quantitative skills that can be demonstrated through their coursework and GRE quantitative score. Ideally, successful candidates should have completed at least 30 quarter credits in mathematics and statistics, including multivariate calculus, linear algebra, and calculus-based probability theory.

Admissions Process

Visit the application guide to learn about our admissions process.

Please note:

This information is intended as an overview, and should be used as a guide only. Requirements, course offerings and other elements may change, and this overview may not list all details of the program. 

Admission requirements listed are departmental requirements, and are in addition to the University's minimum requirements. Many programs receive more applicants than can be admitted, so meeting the minimum requirements for admission does not ensure admission. Every effort is made to ensure minimum admissions requirements are up to date - for the most up-to-date information on the University's minimum requirements, please visit the  UCLA Graduate Division .

Fees are subject to change and should be used as a guide only. For the most up to date fees and more information on fee breakdown, visit the  registrar's office.

More detailed information is available below, in the Department of Biostatistics Student Handbook , and in the School of Public Health's Policies and Procedures Memorandum on the PhD degree .

Program Overview | Entrance Requirements | Student Evaluations | Research and Teaching Assistantships | Seminars | Scientific Minor | Recommended Curriculum | School of Public Health's Policies and Procedures Memorandum on the PhD degree |  School of Public Health Course Search Engine | Academic Ethics

Incoming students without a strong background in basic biology are strongly encouraged to register for the course Introduction to the Biomedical Sciences (260.600), held in advance of the first term each year during the latter part of August. (Click here to search for course times and descriptions.)

Students in the Bloomberg School of Public Health are expected to abide by the highest levels of academic and research integrity.   The Johns Hopkins Academic Ethics Code can be found at:  https://my.jhsph.edu/Resources/PoliciesProcedures/ppm/PolicyProcedureMemoranda/Students_01_Academic_Ethics.pdf

All students must complete an online module to familiarize themselves with this code. (See http://apps2.jhsph.edu/academicethics/Login.aspx?ReturnUrl=/academicethics/WelcomeLoggedIn.aspx ).

As stated in the Academic Ethics Code, "violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations."

For a Biostatistics student, abiding by the Academic Ethics code includes:

• Biochemistry and Molecular Biology

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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]

We create and apply methods for quantitative research in the health sciences, and we provide innovative biostatistics education, making discoveries to improve health.

FIND OUT WHAT'S NEW   MEET OUR STUDENTS   ALUMNI SPOTLIGHTS SEMINARS

Biostatistics Headlines

Alumni spotlight: christopher lo, scm '23.

Christopher Lo, ScM ’23, is a data science trainer in the Data Science Lab at the Fred Hutch Cancer Center where he teaches biomedical data science to the Fred Hutch Cancer Center community.

Noted Biostatistician and Epidemiologist Jim Tonascia Retires

Jim Tonascia, whose public health career in biostatistics and epidemiology spanned more than five decades, retired from the Bloomberg School of Public Health this August.

Student Spotlight: Alyssa Columbus

Alyssa Columbus is a second-year PhD student in the Department of Biostatistics with an interest in public health informatics and data science, including educational interventions, ethical considerations, and policy implications.

What We Do in the Department of Biostatistics

The Bloomberg School's Department of Biostatistics is the oldest department of its kind in the world and has long been considered one of the best. Our faculty conduct research across the spectrum of statistical science, from foundations of inference to the discovery of new methodologies for health applications.

Our designs and analytic methods enable health scientists and professionals across industries to efficiently acquire knowledge and draw valid conclusions from ever-expanding sources of information.

Biostatistics Highlights

First in u.s..

First freestanding statistics department in the U.S.

Data science driving health and empowering opportunity

Foundational discoveries for inference and modeling

Creative, close-knit community

Biostatistics Programs

The Department of Biostatistics offers three graduate programs to applicants with a bachelor's degree (or higher) interested in professional or academic careers at the interface of the statistical and health sciences.

We also have funded training programs in the  Epidemiology and Biostatistics of Aging for PhD students who are U.S. citizens or permanent residents.

Our one-year MHS program provides study in biostatistical theory & methods. It is also open to students concurrently enrolled in a JHU doctoral program.

Master of Science (ScM)

Our ScM targets individuals who have demonstrated prior excellence in quantitative or biological sciences and desire a career as a professional statistician.

Our PhD graduates lead research in the foundations of statistical reasoning, data science, and their application making discoveries to improve health.  

Nilanjan Chatterjee, PhD

Bloomberg Distinguished Professor Nilanjan Chatterjee, PhD, MS, models disease risk associated with genetics, lifestyle, biomarkers, and other factors, with the goal of improving disease prevention. Chatterjee recently received a GKII-KCDH Breakthrough Research Grant on Digital Health. His winning research proposal with Saket Choudhary will involve development of the first risk prediction model and clinical tool for the Indian population.

Biostatistics Consulting Center

The Johns Hopkins Biostatistics Center is the practice arm of our Department, providing the latest in biostatistical and information science expertise to a wide range of clients both within and outside Johns Hopkins.

Alyssa Columbus, Second-Year PhD Student

Alyssa Columbus is a second-year PhD student with an interest in public health informatics and data science, including educational interventions, ethical considerations (e.g., privacy and security) , and policy implications.

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Data is transforming health care, just as it’s transformed nearly every other field of endeavor. However, data is a resource whose value is realized only after it’s been processed and analyzed. Biostatisticians play key roles in unlocking the potential of data to revolutionize the health care industry and enhance the health and well-being of individuals and communities.

Biostatistics applies statistical theory and mathematical principles to biological and health data to help identify the causes of disease, devise effective treatments and implement preventive measures. The growing importance of biostatistics is evident in the wide variety of biostatistics careers available to people who possess the skills and experience to convert the ever-expanding quantity of medical and health data into insights that support the decisions that policymakers and health care professionals make.

These are just three examples of the roles biostatistics can play in improving health systems and individual outcomes:

• The Helmholtz Center for Infection Research applies biostatistics techniques to forecast the potential of health systems to be overwhelmed by COVID-19 patients .
• Biostatistics measured the effectiveness of public health interventions in Mozambique to address sanitary conditions and improve patient outcomes.
• Statistical models identified characteristics associated with short- and long-term survival rates for people diagnosed with colorectal cancer.

What Is Biostatistics?

The short answer to the question, “What is biostatistics?” is that it’s the field of medicine focused on the application of statistical methods and principles to the study of biology. The terms “biostatistics” and “biometry” are often used interchangeably , although biometry’s broader definition encompasses all the biological sciences, while biostatistics is typically limited to medical applications.

The International Biometric Society defines biometry as the science concerned with the development and use of statistical and mathematical methods to solve problems in biology via data analytics. Biostatistics encompasses such health-related fields as genetics, genomics, neuroscience, environmental health and pharmaceuticals. However, the work of biostatisticians touches every aspect of medicine and health care: from pharmaceutical research seeking new treatments to clinical research measuring the effectiveness of cutting-edge therapies for combating cancer, heart disease and other maladies.

Converting Medical and Health Data into Insights

At its most fundamental level, biostatistics is the field that converts data related to disease, medicine and public health into insights that help health care professionals and health policymakers address the challenges related to the health of individuals and communities. However, for the data to be useful and trustworthy, it must be validated :

• Complete: The degree to which all required data is known.
• Clean: Data errors are corrected or removed, irrelevant data is excluded and duplicate data is excised.
• Contextualized : Data values are confirmed to be appropriate for their context.
• Normalized : Data looks and reads the same across all records in the data set.

Primary sources of data for biostatistics applications include electronic medical record (EMR) and electronic health record (EHR) systems. Accessing useful data for creating statistical models and using other analytics tools requires open systems and consistent data formats across health care organizations. New cloud-based data analytics platforms hold promise for overcoming obstacles related to file formats, timely data sharing and securing patients’ private information.

How Biostatistics Contributes to Medical Research and Treatment

Biostatisticians contribute to researchers developing more effective treatments for cancer and infectious diseases, as well as those working in environmental health and behavioral sciences. Among the ways that biostatistics supports cutting-edge medical research are:

• Clinical trials: Help design studies, optimize sample sizes, choose data collection methods and clean data.
• Public health programs: Assist health officials in government, nonprofit organizations and hospitals in understanding the significance of public health research.
• Epidemiological studies: Contribute to research by public health professionals into the factors that influence the causes, behavior and distribution of disease outbreaks, such as COVID-19.
• Meta-analysis and evidence-based medicine: Perform systematic reviews of medical research on specific topics to integrate results and identify potential outcomes that can be applied to develop evidence-based health care.
• Genome sequencing: Cut through the complexity of the massive amounts of data generated by genome sequencing to distinguish genetic traits and variants that may cause disease.

How Advanced Statistical Methods Benefit Medicine and Health Sciences

Generating valid and reliable results requires choosing the correct statistical methods and approaches for the unique characteristics of each research study. Other researchers must also be able to reproduce the research results. Among the techniques that biostatisticians employ are:

• Multiple testing problem (multiplicity) : Biostatisticians must account for the damage that an error in the data has caused that could result in a false positive being inflated when a set of hypotheses is tested simultaneously within a single study.
• Bayesian analysis : Bayesian analysis is a statistical technique that uses probability statements to answer questions about unknown parameters.
• Model averaging : In model averaging, several models run simultaneously, either to make predictions or infer parameters.
• Causal inferences : Causal inference is a form of inductive reasoning that concludes that some entity is or isn’t likely to be the cause of something else.
• Estimation in disease state changes : Morbidity measures the incidence (number of persons who become ill) and prevalence (number who are ill at a given time) of a disease, injury or disability. Estimation is used to calculate incidence proportion (risk) for new diseases or injuries.

What Does a Biostatistician Do?

The primary role of biostatisticians is to apply mathematical and statistical techniques to determine the causes of diseases, injuries and other health issues. More specifically, what a biostatistician does is work as part of a team of researchers and health care providers to discover more effective approaches to the treatment and prevention of common illnesses, including the following:

• Devising more effective solutions to health problems
• Developing faster and more affordable treatment options
• Discovering new ways to apply data analytics tools to help solve problems related to individual health care and public health

Duties and Responsibilities of Biostatisticians

The roles of biostatisticians frequently overlap with those of medical informaticians, bioinformaticians and epidemiologists. The four broad categories of biostatistician tasks and responsibilities are clinical trials, interventional studies, statistical genetics, and systematic reviews and meta-analyses.

• Biostatisticians participate in the design of clinical trials as well as in the analysis of the data that they generate. This includes determining the protocol, preparing case report forms, and writing interim and final reports.
• For observational studies , biostatisticians apply mathematical equations to explain the relationships between variables, whether via multiple measures of the same subject over time or studies of multiple patients interacting with different departments in a health care facility.
• When working on projects related to statistical genetics , biostatisticians integrate findings from mathematics, statistics, genetics, epidemiology and bioinformatics. This requires a background that encompasses a range of disciplines and familiarity with various modeling techniques.
• Determining the level of evidence present in medical research studies requires systematic reviews and meta-analysis that biostatisticians conduct to identify the value of the research results to other researchers and health care professionals.

The education and training required to become a biostatistician begins with earning a master’s degree in biostatistics or public health with concentrations in biostatistics and epidemiology. The most common skills of biostatisticians include mathematics and statistical analysis, problem-solving, critical thinking, communication and teamwork. Among the technical skills that may be required to qualify for a position as a biostatistician are:

• SAS , R and other statistical programming languages
• Relational and nonrelational databases
• Ruby, Python and other general programming languages

Biostatisticians rely on various tools in their work, which includes designing statistical studies and applying advanced analysis techniques to extract intelligence from massive health care datasets. Here are 10 popular statistical tools that biostatisticians use, according to Kolabtree: Stata, R, GraphPad Prism, SAS, IBM SPSS, MATLAB, JMP, Minitab, Statista and Microsoft Excel.

Typical Workday of a Biostatistician

Biostatisticians usually work a standard 40-hour week, although they may work overtime to meet deadlines for specific projects. While biostatisticians do much of their work on computers, their job requires interacting with team members, writing reports, and other communication and interpersonal duties. The profession allows people to contribute to improving the health of people in their communities without participating directly in their treatment.

• Typical data sources for medical and health statistics include surveys, administrative and medical records, claims data, vital statistics from government agencies, surveillance, disease registries and peer-reviewed literature.
• Among the statistical analysis tools that biostatisticians use are the IBM Statistical Package for the Social Sciences (SPSS), MATLAB , GraphPad Prism and Microsoft Excel.
• Collaboration with other researchers , health care professionals and public officials is a vital part of the work that biostatisticians do and the focus of much of their training.

Common Biostatistician Projects and Work Environments

Biostatisticians are employed by private companies, research foundations, educational institutions and government agencies. They spend much of their time working on computers in offices and research facilities as part of a team of researchers, scientists and other professionals in public health and health-related fields. In addition to projects involving the use of SAS, R and other statistics and data analytics tools, biostatisticians participate in these research tasks :

• Study design and protocol development
• Quantitative and qualitative research projects
• Data analysis and interpretation for clinical trials
• Data administration and management
• Public health modeling of disease outbreaks
• Survival analysis for new drugs and treatment approaches
• Institutional review boards (IRBs) to vet the ethics of research procedures
• Research on the effectiveness of cancer treatments

Resources on the Roles and Responsibilities of Biostatisticians

• Pubrica Academy, Role of Biostatistics and Responsibilities of Biostatisticians in Clinical Medical Research — A description of biostatistics applications and the contributions of biostatisticians to medical research projects.
• CROS NT, How Well Do You Understand the Role of Biostatisticians in Medical Research? — An explanation of biostatisticians’ work for nonstatisticians who are members of clinical research teams.

Is Biostatistics a Good Career?

The American Statistical Association (ASA) predicted that 2021 would be “ a year of opportunity for statisticians ” because of growing demand for advanced data analytics skills in pharmaceutical manufacturing, government and other large sectors of the economy.

• Statistician is rated the sixth best career in S. News & World Report ’s listing of the 100 best jobs of 2021. It’s also rated the fifth best science, technology, engineering and math (STEM) job and the second best business job.
• The U.S. Bureau of Labor Statistics (BLS) identifies statistician as the fourth fastest growing career on its list of the 20 fastest growing occupations between 2019 and 2029; the number of jobs is forecast to increase by 35% in that period.
• BioSpace estimates that employment opportunities for biostatisticians will increase by 31% in the U.S. between 2019 and 2028.

Career Options for Biostatisticians

Entry-level positions in biostatistics are available in all areas of medicine and scientific research, including medical assistant, research fellow, software engineer, laboratory technician and instructor. The employment site Zippia describes the 10 best jobs for people entering the field of biostatistics:

• Data analyst
• Biostatistician
• Data scientist
• Statistician
• Software engineer
• Research analyst
• Research internship
• Bioinformatics analyst
• Bioinformatics scientist

Careers in biostatistics involve working in one or more of four areas: clinical trials, public health programs, genome sequencing research and epidemiological studies.

Participating in Clinical Trials

Clinical trials evaluate the effectiveness of medical, surgical and behavioral interventions on patients recruited specifically for the studies. The trials also help determine whether specific treatments have more or less harmful side effects than existing approaches. Some trials attempt to identify diseases before symptoms arise or to prevent diseases entirely; they may also study the role of caregivers and support groups in treating and preventing illnesses.

Contributing to Public Health Programs

The goal of public health programs is to ensure conditions in which people can be healthy. Biostatistics plays a pivotal role in these programs by addressing all three core public health functions :

• Assessment identifies problems that threaten a population’s health and the extent and seriousness of that threat.
• Policy development prioritizes the problems, determines possible intervention and prevention measures, implements policies and regulations to address the problems, and predicts the effects of the policies on the at-risk population.
• Assurance puts in place the services necessary to achieve the goals of the policies and regulations and monitors the community’s compliance with the policies.

Conducting Genome Sequencing Research

Biostatistics is central to the work that medical researchers do to create The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) to support cancer genomics:

• To produce somatic variant call sets from exome (TCGA)
• To create whole genome-level models (ICGC)

The two programs rely on “ scientific crowdsourcing ” to aggregate whole genome sequencing data from 2,658 cancers representing 38 tumor types. The result was 746 samples that serve as benchmarks for comparing exome and genome somatic variant detection techniques.

Performing Epidemiological Studies

A timely example of biostatistics applied to determine the causes and effects of diseases is the work that medical researchers have done to measure the effectiveness of vaccines that prevent COVID-19 . Statistical techniques were used to estimate the effects of the vaccines in case-control and test-negative frameworks while accounting for bias in the results.

The research findings serve as guideposts for public health agencies and health care providers investigating the most effective strategies for measuring the impact and effect of COVID-19 vaccinations on specific populations, including asymptomatic individuals.

Industries in Which Biostatisticians Are Employed

Biostatisticians are qualified to work in any setting that relies on calculating risk and predicting outcomes .

Most careers in biostatistics focus on one of four specific fields of medicine : epidemiology, public health, pharmaceuticals and genetics:

• Among the biostatistics specialties within epidemiology are environmental, genetic, social and nutritional statisticians. Typical activities include determining the rates of infectious and chronic diseases and tracking outbreaks of disease.
• The goals of public health statisticians are to prevent disease and promote long, healthy lives. They work on ways to improve sanitation, prevent infectious diseases, and educate the public on health and hygiene topics.
• Pharmacology statisticians support drug discovery activities, as well as drug development, approval and marketing. They participate in clinical trials, preclinical research and other aspects of drug development.
• Biostatistics is applied in genetics research to automate the process of identifying sequences that may indicate abnormalities causing birth defects and other health problems.

Biostatisticians work in offices, laboratories and in the field conducting a range of tasks, from designing research studies to analyzing and reporting on their results. In addition to technical and statistical skills, Indeed reports that biostatisticians require several personal and professional skills: written and oral communication, problem-solving, critical thinking, ability to work autonomously and adaptability.

Importance of Keeping Current on Biostatistics Tools and Techniques

Machine learning and other areas of artificial intelligence (AI) will continue to impact the way medical and health data is analyzed. The techniques will also facilitate gathering and processing the large quantities of data that will be generated in the future. Increased automation will enhance the use of seven different types of statistical analysis .

• Descriptive statistical analysis is a straightforward summary of the data and its characteristics, with no attempt to infer anything from the analysis.
• Inferential statistical analysis derives inferences from the data to suggest from the sample data what the population at large may think or how they may act, for example.
• Predictive statistical analysis applies statistics and mathematical models to current and past data to predict what will occur or how something will perform.
• Prescriptive statistical analysis goes beyond prediction to recommend specific actions based on its analysis of current and past data. The actions suggested may be in the short or long term.
• Exploratory data analysis performs a cursory analysis of the data prior to the full-scale investigation as a way to check assumptions, identify potential errors and spot patterns.
• Causal analysis attempts to explain why health-threatening events, such as COVID-19, occur and spread through a population.
• Mechanistic analysis examines the precise biological mechanisms that caused actions, as well as the various responses to those actions, as a way to discover how to reduce adverse reactions.

Resources on Careers in Biostatistics

• American Statistical Association, Career Resources — The professional association provides links to salary information, fellowships and grants, funding, ethics, and job opportunities.
• The Balance Careers, “How to Become a Biostatistician” — Among the topics covered are typical work hours, qualifications and related careers.

Biostatistics vs. Bioinformatics

While much overlap exists between biostatistics vs. bioinformatics, the two fields are distinct primarily in the scope of the projects they’re applied to.

• Bioinformatics uses computational technology to organize and analyze the huge data sets that studies in molecular biology are generating, including gene sequencing; gene expression studies; and pharmacogenomics , which studies how genes impact the way a person responds to drugs.
• Biostatistics has a much broader scope, covering the use of statistics and mathematical modeling to analyze and evaluate research relating to public health, medicine, biology and environmental health.

Biostatistics vs. Bioinformatics: Education and Skills

Most biostatisticians earn a master’s degree in biostatistics or public health with concentrations in biostatistics and epidemiology. The educational background required to qualify for positions in biostatistics is weighted heavily toward statistics, mathematics, programming, life sciences and physical sciences. The profession also calls for strong communication and interpersonal skills because much of the work entails collaborating with team members from diverse backgrounds.

For careers in bioinformatics, the educational requirements include a strong background in molecular biology and genetics as well as several bioscience specialties within those categories:

• Cell biology
• Comparative genomics
• Genetic mutations
• Chromosomes and gene expressions
• Molecular cloning
• Immunogenetics
• Gene mapping
• Gene sequencing
• Protein synthesis
• DNA and RNA

Bioinformaticists must be familiar with many different software tools , including the Genome Analysis Toolkit (GATK); the Blast and Bowtie sequence alignment systems; and Partek and other programs for sequencing, microarray and data analysis. Bioinformaticists and biostatisticians both rely on statistical analysis tools, such as SAS and IBM SPSS, as well as programming and machine learning skills.

Biostatistics vs. Bioinformatics: Roles and Responsibilities

Bioinformatics focuses on the collection and analysis of genetic codes and other complex biological data. Conversely, biostatistics emphasizes the design, implementation, analysis and interpretation of studies designed to improve medical treatments and public health in general.

• Bioinformaticists typically work on large databases of omics data: a subset of biotechnology that studies the functions and characteristics of specific biological processes. These include genomics for genes, proteomics for proteins and metabolomics for metabolic functions. The Human Genome Project is an example of the size and scope of such projects.
• Biostatisticians generally are involved in a broader range of topics related to medical and clinical research. They contribute to the design of studies, the protocols to be followed, and monitoring of ongoing research to ensure safety and efficacy.

Resources on Biostatistics vs. Bioinformatics

• Yoh, “Bioinformatics vs. Biostatistics: What’s the Difference?” — An explanation of the close links between the two fields as well as their primary distinctions in terms of broad vs. narrow focus of study.
• National Cancer Institute Center for Cancer Research, Bioinformatics Training and Education Program: Resources — Resources including support programs and collaborations, bioinformatics software resources, and sequencing facilities.

Biostatistics Salary

The BLS estimates that the median annual salary for statisticians working in research and development in the physical, engineering and life sciences was $102,370 as of May 2020. For biostatistics salaries in particular, the BLS reports that statisticians in insurance and related fields had a median annual salary of $88,450, and those working in health care and social assistance earned a median annual salary of $79,440.

The salary survey site PayScale reports that annual salaries for biostatisticians range from about $67,000 for people with one year or less of experience to approximately $129,000 for those with 20 years or more of experience. The median annual salary for all biostatisticians was about $77,000 as of July 2021, according to PayScale. For senior biostatisticians , the median annual salary was around $110,000.

Skills That Can Boost a Biostatistician’s Salary

According to figures that PayScale compiled, specific skills can affect biostatisticians’ salaries:

• Machine learning: 60% higher than the average salary
• Bioinformatics: 27% higher
• Research analysis: 8% higher
• Clinical research: 3% higher

Median annual salaries for biostatisticians who possess certain skills include the following (all figures as of July 2021):

• Clinical research: about $79,000
• Statistical analysis: about $79,000
• SAS: about $77,000
• Data analysis: about $76,000

With experience, biostatistician salaries increase at a steady rate:

• With 5 to 9 years of experience: $88,000
• With 10 to 19 years of experience: $98,000
• With 20 or more years of experience: $129,000

Biostatistics Job Outlook

The results of the ASA’s  2020 Work and Salary survey indicated that statisticians were happy with their chosen occupation:

• 55% of statisticians responding to the survey reported being very satisfied with their primary job, and an additional 36% were somewhat satisfied.
• 65% reported being very satisfied with their job security, 41% were very satisfied with their pay and 31% were very satisfied with their opportunities for advancement.
• The two work attributes that the survey respondents rated very important were doing interesting and enjoyable work (83%) and doing work that makes a positive contribution (73%).

This high level of job satisfaction is mirrored in the BLS’s job outlook for the profession: While growth for all statisticians is estimated to be 35% between 2019 and 2029, the health care industry’s and public policymakers’ growing reliance on data analytics will drive much of that increase, according to the BLS.

Data’s Growing Importance to Healthy Populations

The ever-increasing amount of data being collected about health, disease and genetics has the potential to lead to breakthroughs in public health and the health of individuals. This increases the importance of the role of biostatisticians in converting medical and health data into knowledge and intelligence that health care professionals and public health policy decision-makers can use.

The World Bank’s World Development Report 2021: Data for Better Lives identifies three pathways for the use of data to promote the health and well-being of communities and populations.

• The top pathway uses data to monitor the effects of government policies and individuals’ access to health care and other public services.
• The middle pathway uses data to support evidence-based policymaking and to improve the delivery of public services.
• The bottom pathway uses data to drive growth in the private sector’s provision of services.

Biostatistics plays a key role in enhancing the quality and accessibility of health care and promoting disease prevention through scientific and clinical research and the development of consistent and effective public health policies. Achieving these goals will require innovative approaches to repurpose and combine data sources in ways that are open, transparent and able to meet the needs of all stakeholders in public and private health care.

Biostatisticians as Key Contributors to a Healthier Future

The many challenges to public health brought to light as the world joined together to combat the COVID-19 pandemic also point toward solutions that new technologies have driven that can transform data into actionable insight. Making these insights available to decision-makers in health care and public policy depends on the work of biostatisticians. The result of their work is improved patient outcomes; prospering communities; and healthier, happier, more productive individuals. Individuals who are interested in a biostatistician career should consider pursuing a master’s degree in biostatistics or public health to develop the knowledge and skills to excel in the field.

Infographic Sources:

Indeed, “Learn About Being a Biostatistician” Kolabtree, “Top 10 Statistical Tools Used in Medical Research”

Learn More About Our MPH Program

• Current Students

Biostatistics Division

Study the factors that determine the distribution of health and disease in human populations, and improve the understanding of data that are relevant to issues in public health

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 the fundamental concern of the Group in Biostatistics. The program offers training in theory of statistics and biostatistics, computer implementation of analytic methods, and opportunities to use this knowledge in areas of biological/medical research. The resources and facilities in the School of Public Health and the Department of Statistics, together with those of other university departments, offer a broad set of opportunities to satisfy the needs of individual students. Involvement of faculty from the Department of Biostatistics & Epidemiology at UC San Francisco enriches instructional and research activities.

On-Campus Biostatistics Programs

Many issues in the health, medical and biological sciences are addressed by collecting and exploring relevant data. We offer training in the theory of statistics and biostatistics, computer implementation of analytic methods, and opportunities to use this knowledge in areas of biological and medical research.

• Biostatistics PhD
• Biostatistics MA
• Biostatistics programs recruitment guide

On-Campus Epidemiology/Biostatistics Program

Our program offers a curricular track for students to acquire proficiency in both biostatistics and epidemiology. Our training emphasizes the development of skills applicable to the study of the occurrence and distribution of disease, focusing on determining the impact and magnitude of disease frequency so that effective control measures can be designed.

• Epidemiology/Biostatistics MPH

Online MPH - Epidemiology/Biostatistics Concentration

This is a 27-month online master of public health in epidemiology and biostatistics.

We train students to study the factors that underlie health and disease in human populations, including the analysis of data to develop and evaluate strategies for disease prevention and control. In this online program, students will acquire proficiency in both epidemiology and biostatistics and will be able to immediately apply these skills to improve the effectiveness of public health programs.

• Berkeley Public Health Online

Biostatistics Faculty

Emeriti faculty, 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.

Mission Statement

The mission of the Biostatistics DEI Committee is to challenge systemic inequities in the areas of health, medical, and biological sciences. Along with other divisions in the School of Public Health and the Dream Office , the Group in Biostatistics is working to reduce barriers to entry in biostatistics and is committed to fighting against racism, prejudice, and discrimination in order to create welcoming, equitable, and inclusive environments of learning and research.

Diversity, Equity and Inclusion Initiatives

The Biostatistics Division is committed to making our community more diverse, equitable, and inclusive. Here is a non-exhaustive list of the current Diversity, Equity and Inclusion initiatives:

Since Spring 2021, we introduced a peer mentorship program to build stronger connections between senior and junior graduate students so that they can thrive in graduate school together. We hope this enables senior students to connect with and help first year students navigate the program by providing advice and answering questions as they transition from coursework to research.

The Biostatistics Division has participated in the graduate diversity admissions fair at University of California, Berkeley since 2020. In 2021, we are partnering with the Department of Statistics to launch Zoom panels to introduce the program to undergraduate students who belong to groups historically underrepresented in the graduate programs in related fields.

\We are maintaining a collection of service opportunities , with a particular emphasis on STEM mentorship/tutoring programs for underrepresented groups in the field. Please directly comment on the spreadsheet using a Berkeley account to suggest additions or corrections to the list. This includes opportunities at UC Berkeley and in the broader East Bay community. All graduate students and postdoctoral scholars in related fields are encouraged to explore these service opportunities if they are interested.

For more information, contact DEI faculty advisor Corinne Riddell .

• Doctor of Philosophy

Doctor of Philosophy Courses & Timeline

Most students take the core lecture courses during their first two years. Timing of electives vary from what is listed.  Please discuss when to take your electives with your advisor.

Standard Pathway

Statistical genetics pathway, sample timeline for standard pathway.

Core Courses

• BIOST 504 Foundations of Public Health for Biostatistics (2 credits) or PHI 500 Public Health Practice, Science and Knowledge (1 credit)
• BIOST 514 Biostatistics I (4 credits)
• BIOST 515 Biostatistics II (4 credits)
• BIOST 533 Theory of Linear Models (3 credits)
• BIOST 570 Advanced Regression Methods for Independent Data (4 credits)
• BIOST 571 Advanced Regression Methods for Dependent Data (4 credits)
• BIOST 572 Advanced Regression Methods: Project (3 credits)
• STAT 512 Statistical Inference (4 credits) *
• STAT 513 Statistical Inference (4 credits) *
• STAT 581 Advanced Theory of Statistical Inference (4 credits)
• STAT 582 Advanced Theory of Statistical Inference (4 credits)
• STAT 583 Advanced Theory of Statistical Inference (4 credits)

*A new student placement exam may be taken to waive  STAT 512  and  STAT 513 .

Additional requirements

• BIOST 580 Seminar in Biostatistics (9 credits total)
• BIOST 590 Biostatistical Consulting (3 credits)
• BIOST 591 Applied Research Project (3 credits)
• BIOST 800 Doctoral Dissertation (36 credits total)
• Minimum 6 credits from  Elective List One: Methodological Emphasis
• Minimum 8* credits from  Elective List Two: Biology or Public Health Emphasis .
• *Minimum 9 credits are required for entering cohorts of 2022 and earlier.
• Students who entered the department in Autumn 2018 or prior fulfill this requirement by taking at least one course (3 credits) in epidemiology. This can be satisfied by taking a BIOST course that is cross-listed with Epidemiology (e.g.,  BIOST 516 ,  BIOST 519 ,  BIOST 520 , BIOST 531 ,  BIOST 536 ,  BIOST 537 ,  BIOST 555 ) as well as  EPI 517  (cross-listed with PHG 511). Note: a BIOST course that is cross-listed with EPI and that is listed either under Elective List 1 or 2 counts towards both the electives credit and the epidemiology requirement.
• Students who enter the department in Autumn 2019 through Autumn 2022 fulfill this requirement by taking BIOST 504 Foundations of Public Health for Biostatistics as one of their List 2 electives, satisfying the most recent requirements set forth by the Council on Education for Public Health (CEPH) .
• Elective courses may be taken S/NS or equivalent, pending approval from the Graduate Program Director. See policy here: https://www.biostat.washington.edu/support/policy-graduate-students-bio… . To earn the grade of "S," students must achieve a 2.7 or higher.

Course recommendations and notes

• The department recommends that students register for and attend the BIOST 580 Seminar in Biostatistics  every quarter. Students are required to register for BIOST 580 for at least nine quarters.
• In preparation for work on their dissertation, students are expected to master computational skills at the level covered in  BIOST 561 Computational Skills for Biostatistics . Taking this course in the first year of PhD studies is recommended.
• In preparation for advanced coursework in Statistical Theory ( STAT 581  ,  STAT 582 , and  STAT 583 ) and the PhD Statistical Theory Exam, students are expected to master Real Analysis and Measure Theory at the level covered in  MATH 574 ,  MATH 575 , and  STAT 559 . Taking these courses in the first year of PhD studies is recommended, for students who have not previously mastered this material.
• In preparation for the PhD Applied Research Project (BIOST 591), students are expected to master data analysis at the level covered in  BIOST 579 Data Analysis and Reporting . Taking this course at least once in the second or third year of PhD studies is recommended.
• BIOST 590 Biostatistical Consulting  is a required course and is typically taken during Years 3 or 4. Enrollment is limited to 4-6 students per quarter so advance planning is necessary. Students may email  [email protected]  to sign up for a quarter.
• Most students will complete The Applied Research Project ( BIOST 591 ) after the 570s sequence, the Data Analysis course (BIOST 579) and the Consulting Course (BIOST 590), as these all provide important preparation. A project proposal that has been approved by the Applied Project Committee is required prior to registering for BIOST 591. BIOST 591 must be completed prior to the General Exam.

Optional courses

• BIOST 582 Student Seminar is offered every Autumn, Winter, and Spring quarter.
• BIOST 600 Independent Study may be arranged with a faculty advisor in any quarter.
• First-Year Theory Exam follows spring quarter of Year 1.
• PhD Theory Exam occurs at the end of Summer Year 2.
• Selection of dissertation advisor and topic by Year 3. (This process includes conducting independent studies with faculty, attending seminars, and engaging in discussions with faculty).
• Applied Research Project (BIOST 591) completed before General Exam in Years 3 or 4.
• General Exam completed in Years 3 or 4.
• Final Exam completed in Years 4 or 5.

Sample Timeline for Statistical Genetics Pathway

Theory, Methods, and Applications

Statistical genetics.

• BIOST 550 Statistical Genetics I: Mendelian Traits (3 credits)
• BIOST 551 Statistical Genetics II: Quantitative Traits (3 credits)
• GENOME 540 Introduction to Computational Molecular Biology: Genome and Protein Sequence Analysis (4 credits) or GENOME 541 Introduction to Computational Molecular Biology: Molecular Evolution (4 credits)
• GENOME 562 Population Genetics (4 credits)
• BIOST 581 Statistical Genetics Seminar (9 credits total)
• Minimum 6 credits from  Elective List One: Methodological Emphasis
• Minimum 2 credits* from  Elective List Two: Biology or Public Health Emphasis .
• *Minimum 1 credit is required for entering cohorts of 2022 and earlier.
• BIOST 571  and  BIOST 572  (which is required for Standard Pathway students) may count as Elective List One credits for Stat Gen students.)
• Elective courses may be taken S/NS or equivalent. To earn the grade of "S," students must achieve a 2.7 or higher.
• Nine quarters of BIOST 581 Statistical Genetics Seminar  are required, and the department recommends that students register for and attend BIOST 581 every quarter.
• In preparation for work on their dissertation, students are expected to master computational skills at the level covered in BIOST 561 Computational Skills for Biostatistics . Taking this course in the first year of PhD studies is recommended.
• In preparation for advanced coursework in Statistical Theory ( STAT 581  ,  STAT 582 , and  STAT 583 ) and the PhD Statistical Theory Exam, students are expected to master Real Analysis and Measure Theory at the level covered in MATH 574 ,  MATH 575 , and  STAT 559 . Taking these courses in the first year of PhD studies is recommended, for students who have not previously mastered this material.
• BIOST 590 Biostatistical Consulting   is a required course and is typically taken during Years 3 or 4. Enrollment is limited to 4-6 students per quarter so advance planning is necessary. Students may email  [email protected]  to sign up for a quarter.
• GENOME 562 Population Genetics is a required course for the pathway and is offered winter quarter in odd numbered years. Students without a background in genetics may need to take an introductory course prior to this class.
• GENOME 540 Introduction to Computational Molecular Biology: Genome and Protein Sequence Analysis (or  GENOME 541 Introduction to Computational Molecular Biology: Molecular Evolution ) is a required course for the pathway and is offered winter quarter. Students typically take it during Years 2 or 3. Students without a background in genetics may need to take an introductory course prior to this class.
• The PhD Theory Exam occurs at the end of Summer Year 2.

Selecting the Pathway

Students must email the Graduate Program to notify of their wish to follow the Statistical Genetics PhD pathway.

Department of Biostatistics

Phd in biostatistics, degree description and learner objectives.

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

The program requires competency in the theory of statistics and probability, in introductory and advanced biostatistical methods and theory, and in fundamentals of epidemiologic study design. The doctoral dissertation will be the culminating experience in the Ph.D. program. Graduates of the doctoral program will have written a doctoral dissertation which focuses on the development of a new methodology or on the innovative application of biostatistical methods to a health sciences research problem.

Graduates of the Ph.D. program will be in a position to:

• Demonstrate an increased level of knowledge and understanding of current statistical theory, methods, and practices in the health sciences
• Develop new statistical methods
• Design, manage data, analyze and interpret data from a variety of experimental and observational studies
• Communicate research findings, including new statistical methods developed, effectively to various audiences in writing and through oral presentation

The goals of the Ph.D. program are to train students in the application of appropriate statistical methods for diverse problems in medicine and public health, and to provide a solid theoretical foundation for the development and investigation of new statistical methods. In addition to the formal statistical training, students will have adequate flexibility in choosing statistical and non-statistical electives to tailor their curriculum towards a specific application area such as genetics, epidemiology, or environmental health.

Graduates of the Ph.D. program in biostatistics will have:

• The ability to develop careers in academia, research institutes, government, and industry;
• A broad understanding of current statistical methods and practices in the health sciences;
• 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;
• Experience in writing reports and giving oral presentations describing health science studies.

Prerequisites

The entrance requirements are the same as stated for the master’s degree. In addition, completion of an M.S. program in biostatistics or statistics, either at the University of Iowa or elsewhere, is generally required.

Course Requirements

M.S. Level Background: 33 s.h.

Ph.D. students must take the following 33 s.h. of Required Courses listed in the M.S. Program in Biostatistics:

CPH:6100, BIOS:5510, BIOS:5710, BIOS:5720, BIOS:5730, BIOS:6610, BIOS:7270, BIOS:7500, EPID:4400, and STAT:4100/4101 (or STAT:5100/5101).

(Students may request waivers and/or transfer of credit if they have already had the material at another institution. Course credits are automatically transferred for students who received their M.S. in Biostatistics from the University of Iowa.)

Core courses (17 semester hours required) (effective fall 2020)

Doctoral students are required to earn a grade of at least a B- in each core course.  If this requirement is not met, the core course must be repeated and a grade of at least B- must be achieved.

Electives and Dissertation  (29 semester hours required)

With approval by a student’s academic advisor, students should choose 16-23 s.h. of courses from the following list. Other courses may count as electives, but require the approval of the advisor and the DGS.  Independent Study (BIOS:7800) s.h. do not generally count as an elective and requires approval of the advisor and the DGS.  At least 6 s.h. of electives need to be in courses taken for a letter grade.

Recommended, but not limited to, elective courses.

Dissertation Requirement

Total Semester Hours for Ph.D.: 79 s.h.

Ph.D. Comprehensive Examination

The Ph.D. comprehensive examination is offered once yearly. If the examination is not passed in the first attempt, it may be repeated one time. The examination consists of a two-day in-class component (two 3-hour examinations on consecutive days) and a take-home component. The in-class component contains a closed-book set of theory problems drawn from the Ph.D. core courses. The take-home component is comprised of three sections: data analytic problem, simulation problem and an open problem.  In highly unusual circumstances, an oral examination may be given as a follow-up to the written examination if clarification is felt to be necessary by the departmental Comprehensive Examination Committee.  Please refer to the Student Handbook for additional information regarding the Ph.D. comprehensive examination.

Ph.D. Dissertation Prospectus

The dissertation prospectus describes the rationale for the proposed research and outlines its basic components. Prior to initiation of the research, the prospectus is submitted to the student’s dissertation committee members. Please refer to the Dissertation Committee in the Student Handbook for the requirements of the Dissertation Committee membership. A meeting of the committee to evaluate the prospectus is required, and written approval by all committee members is required.

Dissertation Defense

The student and the student’s committee are required to comply with Graduate College guidelines with regard to preparation of the dissertation and meeting Graduate College deadlines for graduation. During the dissertation defense, the dissertation committee will thoroughly examine the student’s knowledge in the content area of the research.

Meet Our New PhD Students!

We’ll be featuring mini-profiles of our new PhD students over the next few weeks. We look forward to welcoming them into our community!

Hello! My name is Madeleine (or Maddy) Carbonneau. I graduated from Harvard in 2020 with my undergraduate degree in applied math. After graduating, I worked for a couple years at Roivant Sciences, a biotech/ pharmatech company in New York. For the last two years, I have been a post- baccalaureate fellow at the Framingham (MA) Heart Study with the National Heart, Lung, and Blood Institute asking questions about epigenetics, lifestyle factors, and biological aging.

Although I left college thinking I wanted to work in business development in the biotech industry, I quickly learned after starting my first job that I did not like being so far away from the research activities of the company. Luckily, my employer gave me the option to transition to more research-oriented roles. Here, I was able to work with powerful datasets to answer interesting questions and I felt much more motivated by my projects.

I started my position at the Framingham Heart Study to do some research in a more-academic environment and prepare for a doctoral program. In my current research, I work with a lot of genetic/multi- omics data and I’m very motivated to research the questions that arise from these data. I am particularly interested in using analytical structures that allow us to understand how genes work in synchrony. Although my research interests remain quite broad, in graduate school, I would like to explore how we can use statistics to better quantify and describe the relationships between genes and how their interrelated (dys)function can cause disease.

Outside of my research, I aim to surround myself with as many dogs as possible. My partner and I have two dogs of our own, Ginger and Sophie, and have fostered several other dogs. When that is not enough dog for me (it rarely is), I volunteer at an animal shelter where I walk and hang out with more dogs. I also enjoy spending time with my family, who lives in the Boston area. Most of all, I love hanging out with their dogs.

Hello! My name is Armelle Duston. I am of French origin but I grew up in the US in Lynchburg, Virginia. This past May, I graduated with a BS in Applied Math and Statistics from Colorado School of Mines.

At Mines, I first came across the field of mathematical biology which would eventually lead me to my interest in biostatistics. From the beginning, I was interested in using mathematical tools to better understand and improve human health.

My main research experience in undergrad was in investigating the circadian rhythms of adolescents under the mentorship of Dr. Cecilia Diniz Behn. This work involved some mathematical modeling work with systems of differential equations, but my main project was a statistical analysis of combined datasets leading to a finding about sex differences in the circadian rhythms of adolescents. Outside of research, I also participated in a summer institute in biostatistics (SIBS) at NC State and Duke. From taking a course on spatial statistics, I developed an interest in statistical methods in epidemiology and particularly environmental health.

In my free time, I love to read, travel, and do a variety of outdoor sports including hiking, mountain biking, and rock climbing. I’m excited to see what Boston has to offer, and I am looking forward to meeting everyone!

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