Penn State’s MS in Biostatistics trains students to apply advanced statistical tools to health and biomedical data. The curriculum includes probability, regression, survival analysis, clinical trials, and longitudinal data modeling. Students work on public health and medical research studies, gaining skills in R, SAS, and Python. The program is ideal for those pursuing research careers in public health, pharma, or further PhD training.
Designing and analyzing randomized controlled trial data
Modeling patient survival times using Cox regression
Comparing machine learning models for cancer risk prediction
Creating a time-to-event model for infectious disease outcomes
Analyzing dose-response data in pharmaceutical studies
Developing mixed-effects models for repeated measurements
Simulating missing data strategies in clinical datasets
Evaluating diagnostic test accuracy using ROC curves
Using Bayesian methods for small sample size studies
Mapping disease clusters with spatial statistical methods
Analyzing gene expression data for cancer subtype classification
Designing adaptive trial protocols with interim analyses
Creating dashboards for real-time hospital performance metrics
Assessing interaction effects in nutritional epidemiology studies
Modeling vaccine efficacy across age-stratified populations
Lead biomedical innovation by designing statistical models for complex, life-saving health data at Penn State.
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