Events Calendar

Daniel F. Heitjan, PhD

Professor of Statistical Science

Southern Methodist University

Professor of Clinical Sciences

UT Southwestern Medical Center

Dallas, Texas

“Real-Time Prediction in Clinical Trials:  A Statistical History of REMATCH”

Randomized clinical trial designs often incorporate one or more planned interim analyses.  In event-based trials, one may prefer 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.  Because an interim analysis can impose a considerable logistical burden, and the timing of the triggering event in this kind of study is itself a random variable, it is natural to seek to predict the times of future landmark events as accurately as possible.

Early approaches to prediction used data only from previous trials, which are of questionable value when, as commonly occurs, enrollment and event rates differ unpredictably across studies.  With contemporary clinical trial management systems, however, one can populate trial databases essentially instantaneously.  This makes it possible to create predictions from the trial data itself — predictions that are as likely as any to be reliable and well calibrated statistically.

This talk will describe work that some colleagues and I have done in this area.  I will set the methodologic development in the context of the study that motivated our research:  REMATCH, an RCT of a heart assist device that ran from 1998 to 2001 and is considered a landmark of rigor in the device industry.