Part I. Introduction to Randomized, Controlled Trials.
1. Introduction. Part II. Analytic Methods for Randomized, Controlled Trials.
2. Dichotomous and ordinal: chi-square and Fisher's exact tests and binary regression models.
3. Continuous: t-test, Wilcoxon-test, and linear or non-linear regression models.
4. Time to event subject to censoring: logrank test, Kaplan-Meier estimation and Cox proportional hazards regression models.
5. Count: Poisson and negative binomial regression models.
6. Longitudinal: Linear and generalized linear mixed models, GEE.
7. Recurrent events.
8. Cross-over design.
9. Factorial design.
10. Cluster randomized design.
11. Randomization, stratification, and outcome-adaptive allocation.
12. Sample size estimation and power analysis: Dichotomous, ordinal, continuous and count.
13. Sample size estimation and power analysis: Time-to-event data subject to censoring.
14. Sample size estimation and power analysis: Longitudinal data.
15. Group sequential methods, triangular methods and stochastic curtailments.
16. Sample size re-estimation.
17. Adaptive designs.
18. Multiple testing.
19. Subgroup analysis.
20. Competing risks.
21. Joint models for longitudinal markers and clinical outcomes.
22. Sequential multiple assignment randomization trial (SMART) for dynamic treatment allocation.
23. Safety data analysis.
24. Non-inferiority trials.
25. Incorporating historical data into RCTs.
26. Validation of surrogate outcomes.