The design and analysis of a randomized clinical trial involves a series of decisions, including the choice of the primary outcome, sample size, randomization algorithm, interim monitoring plan, and the choice of the primary analysis and estimand of interest. This course will focus on the statistical considerations that inform each of these decisions.  Additional topics include addressing multiple comparisons, handling missing data, and whether to consider an adaptive design. We will present a set of simple tools and principles that go a long way towards defining a robust clinical trial design. We will also shed light on some common pitfalls to avoid. Discussions will be driven by examples of trials from a variety of domains including cardiovascular disease, infectious disease (HIV, Ebola, COVID-19), as well as other settings.

We assume enrollees will be familiar with topics taught in introductory statistics (t-tests, regression, confidence intervals, p-values, and a basic understanding of the central limit theorem).