Statistical Methods for the Design and Analysis of Randomized Intervention Trials

This course covers topics relevant for the design, conduct and analysis of clinical intervention trials. The statistical theory behind the methodology as well as practical issues will be discussed. The course is divided into three areas: 1) important methods for the design of randomized controlled trials including randomization techniques, sample size and power calculations, factorial designs, crossover designs, cluster-randomized trials, non-inferiority trials, adaptive designs, group sequential trials, and ethical issues in design and conduct of clinical trials; 2) topics of predictive modeling including ROC curves, explained variation, and biomarker analyses; 3) dealing with missing data in randomized trials through imputation and inverse weighting, missing covariates, non-compliance, contamination, unplanned crossover, and surrogate outcomes. Clinical trials from the medical literature and other sources in the public domain will be used as case studies for illustration. Simulations and data analyses will be carried out using statistical software (e.g. R or SAS). Students will be trained and assessed in part based on the preparation of reports and delivery of presentations.