Michigan State University

What you'll study

The programme combines advanced courses in probability and statistical theory with specialised coursework and research in biostatistical methods. Core topics typically include probability theory, mathematical statistics, linear models and inference, computational statistics and statistical computing. Biostatistics-focused modules commonly cover generalized linear models, survival analysis and censoring methods, longitudinal and correlated data analysis, clinical trial design and analysis, causal inference, high‑dimensional data and penalised regression, Bayesian methods for biomedical applications, and reproducible research and data management.

Students follow a curriculum that balances formal coursework, qualifying examinations, teaching experience and a sustained research programme leading to the doctoral dissertation. Seminar series, journal clubs and collaborative applied projects with laboratory or clinical partners are integral, providing exposure to real datasets and interdisciplinary problems. Students also develop proficiency in statistical software (R, Python, or specialised packages), simulation studies, and the presentation of methods to scientific audiences.

Entry requirements

Applicants are normally expected to hold a strong bachelor’s degree in statistics, mathematics, data science or a closely related quantitative discipline; many admitted students hold a master’s degree. Key components of a competitive application include academic transcripts showing strong performance in calculus, linear algebra, probability and statistics; letters of recommendation that speak to research potential; and a statement of purpose outlining research interests in biostatistics or related areas.

Relevant research or applied experience — for example, work on statistical projects, internships in biomedical settings, or publications — strengthens an application. International applicants must demonstrate English proficiency according to university requirements. The department evaluates each application holistically; specific test requirements (such as the GRE) are subject to departmental policy, so applicants should consult the programme website for current guidance.

Career prospects

Graduates are prepared for research and leadership roles across academia, industry and the public sector. Common career paths include tenure‑track faculty positions in statistics or biostatistics, research scientist roles in pharmaceutical and biotechnology companies, biostatistician or data scientist roles in hospitals and public health agencies, and methodological or consulting positions in contract research organisations (CROs) and government research laboratories.

PhD holders from the programme are equipped to lead collaborative, interdisciplinary teams, develop novel statistical methodology for clinical trials, genomics, epidemiology and longitudinal studies, and translate complex analyses into practical decision‑making for healthcare and life‑science problems.

Why study at Michigan State University

Michigan State offers a research‑intensive environment with opportunities for interdisciplinary collaboration across life sciences, medicine, public health and environmental science. The Department of Statistics and Probability provides faculty expertise spanning theoretical statistics, computational methods and applied biostatistics, enabling students to pursue methodological research alongside applied projects with domain partners.

Students benefit from departmental seminars, a structured mentoring environment, access to high‑performance computing facilities and statistical consulting opportunities that develop collaborative skills. Funding support for doctoral students is commonly available through assistantships or fellowships that combine research and teaching experience. The university’s large research ecosystem and ties with regional health and industry partners create a rich setting for biostatistical research and career development.