9‑Module Curriculum
Biostatistics, Epidemiology & Medical Literature Interpretation
A structured, high‑yield course from foundational epidemiological measures to critical appraisal and research ethics. Each module includes core topics, high‑yield subtopics, and clinical correlations to bridge theory with practice.
Module 1: Epidemiology & Population Health
Measures of disease frequency (incidence, prevalence, duration), health status metrics (mortality, reproductive rates, standardization), population impact (PAR, YPLL, QALYs, DALYs), survival analysis (Kaplan‑Meier, log‑rank), demographics (population pyramids, demographic transition), disease surveillance, outbreak investigation, herd immunity, and levels of prevention.
Module 2: Study Design & Research Methods
Descriptive studies (case reports, case series); observational studies: cross‑sectional, ecological (ecological fallacy), case‑control (odds ratio, matching), cohort (relative risk, prospective/retrospective); experimental studies: RCTs (randomization, blinding, allocation concealment, parallel, crossover, cluster, stepped‑wedge, superiority/non‑inferiority); systematic reviews & meta‑analysis (forest plots, funnel plots, heterogeneity); qualitative research (interviews, focus groups).
Module 3: Measures of Association & Data Analysis
Relative risk (RR), odds ratio (OR), hazard ratio (HR); absolute risk reduction (ARR), number needed to treat (NNT), number needed to harm (NNH); population attributable risk (PAR) and PAR%; interpreting 2×2 tables; calculating and applying these measures in clinical studies.
Module 4: Distributions & Statistical Concepts
Data types (nominal, ordinal, interval, ratio); central tendency (mean, median, mode) and variability (SD, variance, IQR, SE); normal distribution (68‑95‑99.7 rule, Z‑scores); skewed distributions (right/left); binomial, Poisson, bimodal; chi‑square test, Fisher’s exact, McNemar.
Module 5: Correlation, Regression & Probability
Pearson correlation (r) and Spearman (ρ); simple and multiple linear regression (slope, intercept, adjusted coefficients, confounding control, multicollinearity); logistic regression (odds ratios, AUC); probability rules, decision trees, expected value; likelihood ratios (LR+, LR‑), Bayes’ theorem (posttest odds = pretest odds × LR), Fagan nomogram.
Module 6: Screening & Diagnostic Testing
Sensitivity, specificity, PPV, NPV (effect of prevalence); ROC curves and AUC; selecting optimal cutoffs; screening biases: lead‑time, length‑time, overdiagnosis, volunteer bias; Wilson‑Jungner criteria; appropriate screening examples.
Module 7: Study Interpretation & Critical Appraisal
Hill’s criteria for causation (temporality, strength, dose‑response, etc.); hypothesis testing (p‑value, Type I/II error, power, multiple comparisons); confidence intervals (precision, significance); internal vs. external validity; bias (selection, information, confounding) and methods to control confounding; clinical vs. statistical significance; surrogate endpoints.
Module 8: Clinical Decision Making & Evidence‑Based Medicine
PICO framework; GRADE system (quality of evidence, strength of recommendations); shared decision making, communicating risks (NNT/NNH, absolute risk, framing); integrating patient values and preferences.
Module 9: Research Ethics
Belmont Report (respect for persons, beneficence, justice); Declaration of Helsinki; informed consent (voluntary, informed, competent); IRB, HIPAA, Data Safety and Monitoring Board (DSMB), interim analyses; conflict of interest, research misconduct (fabrication, falsification, plagiarism); ICMJE authorship criteria.