Explore Biostatistics at Michigan
Cancer biostatistics brings together statistics, computing, cancer biology, clinical medicine, epidemiology, and patient experience to produce evidence that can improve cancer prevention, diagnosis, treatment, and survivorship.
Answering that question requires more than analyzing a dataset. Biostatisticians work across the cancer-research team to connect scientific questions, patient priorities, study design, complex data, statistical methods, and responsible interpretation.
See biostatistics at the centerBiostatistics at the center
Cancer research brings together many kinds of expertise. Biostatisticians help connect those perspectives by translating scientific and clinical questions into study designs, measurable outcomes, analytical methods, and evidence the full team can interpret.
Biostatisticians contribute across the full research process, from the first formulation of the question through the interpretation and communication of the findings.
Select a collaborator to see how biostatistics connects their expertise to a cancer-research question.
From collaboration to evidence
Biostatisticians work throughout the research process—not only after the data have been collected. Select a stage to explore how statistical reasoning helps turn a cancer question into reliable evidence.
Use what you already know
The subjects required for graduate study become practical tools for answering cancer-research questions.
One problem, several approaches
A new treatment is being developed for an aggressive cancer. Researchers want to know whether it improves survival, which patients benefit, and whether its risks are acceptable.
Questions being studied at Michigan
Biostatisticians help balance evidence of treatment activity against the probability of serious toxicity.
Biomarker and treatment-effect models can help researchers study whether effectiveness differs across patient subgroups.
Survival methods account for different follow-up periods, incomplete observations, recurrence, and competing outcomes.
High-dimensional methods can connect tumor characteristics with prognosis, treatment response, and possible therapeutic targets.
Population and causal methods can help study differences in screening, treatment access, disease stage, and survival.
Longitudinal models help researchers study symptoms, side effects, functioning, and patient-reported quality of life.
An illustrative pathway
This example shows how foundational coursework can lead toward more specialized study. It is not a formal concentration or guaranteed course sequence.
Choose your own direction
Frequently asked questions
Students do not need to arrive as cancer biologists or clinicians. Quantitative preparation is central, while biological and clinical knowledge develops through coursework, seminars, collaboration, and application.
Experience with a language such as R or Python is valuable. Students use programming to manage data, fit models, conduct simulations, create visualizations, and produce reproducible analyses.
No. Michigan offers a substantial cancer-research environment, but individual research placements, assistantships, publications, and faculty-led projects are not guaranteed for MS students.
The Biostatistics MS emphasizes broad statistical theory and methodology with elective flexibility. Health Data Science places additional prescribed emphasis on computing, machine learning, and large health datasets. Students should compare the current curricula and discuss questions with the program.
This page can help prospective students understand cancer biostatistics, connect prerequisite coursework to health research, and explore an illustrative Michigan academic pathway.
It does not guarantee research placement, assistantship funding, publication, a thesis experience, or access to a particular faculty project.