Randomized clinical trials often include planned interim analyses, at which external reviewers assess the accumulated data to determine whether the study should continue. With time-to-event endpoints, it is often desirable to schedule the interim analyses at the times of occurrence of specified landmark events, such as the 100th event, the 200th event, and so on. It can be worthwhile to predict the times of such events, together with other trial outcomes, as an aid to real-time logistical planning.
Traditional prediction methods use data only from previous trials and give inaccurate projections if, as often happens, historical enrollment or event rates differ from those in the current trial. With modern data management systems we can create accurate and complete study databases in real time, making it possible to use the accumulating data from the trial itself to make predictions about its future. Over the last several years the presenters have developed a suite of statistical methods for real-time prediction of the future course of a clinical trial. In this short course we will describe these methods and train potential users in their application.