The Master of Science in Biostatistics at the University of Michigan prepares students to develop and apply statistical methods to biological and health sciences. The program equips graduates to work in medical research, pharmaceuticals, and public health analytics through rigorous training in probability, statistical inference, and computing.
Bayesian Modeling of COVID-19 Transmission in Urban Populations
Survival Analysis of Cancer Patients Using Electronic Health Records
Design and Evaluation of Adaptive Clinical Trials
Predictive Modeling for Hospital Readmission Using Machine Learning
Time-to-Event Analysis in Heart Failure Treatment
Statistical Methods for Longitudinal Data in Diabetes Studies
Development of Risk Prediction Models for Stroke Incidence
Handling Missing Data in Observational Health Datasets
Genomic Data Integration for Cancer Subtype Classification
Hierarchical Modeling of Vaccine Effectiveness Over Time
Propensity Score Matching for Observational Studies in Public Health
Spatial Epidemiology: Mapping Disease Clusters Using GIS
Causal Inference in Healthcare Policy Evaluation
Multivariate Analysis of Biomarkers in Neurodegenerative Disorders
Statistical Approaches for Genotype-Phenotype Associations
High-Dimensional Data Analysis in Bioinformatics
Variance Component Models in Twin Studies
Use of Bootstrapping Methods in Small Sample Clinical Trials
Meta-Analysis of Drug Efficacy Across Randomized Trials
Machine Learning Classifiers for Early Disease Detection
The MS in Biostatistics combines core statistical methods with modern applications in genomics, epidemiology, and big health data, empowering students to drive evidence-based insights across health sectors.
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