Biostatistics
Undergraduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
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Topics typically covered
Standard syllabus
Study design and data
- Observational vs experimental studies in health sciences
- Cohort, case-control, and cross-sectional designs
- Randomized controlled trials: principles and analysis
- Survival and time-to-event data (introduction)
- Handling missing data and attrition
Inference for health outcomes
- Comparing means in clinical trials: t-tests and ANOVA
- Analyzing proportions: chi-square and Fisher's exact test
- Relative risk, odds ratios, and confidence intervals
- Kaplan–Meier estimation (introduction)
- Log-rank test (introduction)
- Sample size and power for clinical studies
Regression in biostatistics
- Linear regression for continuous outcomes
- Logistic regression for binary disease outcomes
- Cox proportional hazards model (introduction)
- Confounding and adjustment in epidemiologic models
- Interaction in public-health regression models
STEM / applied
Applied biostatistics practice
- Analyzing NHANES or similar public datasets
- Meta-analysis concepts and forest plots
- Diagnostic test accuracy: sensitivity, specificity, ROC curves
- Longitudinal data preview: repeated measures
- Ethics, IRB, and reproducibility in biomedical research
- Communicating biostatistical results to clinicians
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Aligns with biostatistics courses in schools of public health and life-science departments. Emphasis on study designs common in clinical and epidemiologic research.