Prediction of event times in clinical trials accounting for dependence of failure times on covariates

Music City Center 

An important practical aspect of a clinical trial with analyses planned at pre-specified event counts, is prediction of the times of these specified landmark events such as the 50th event or the 100th event using the accumulated data from the trial itself. Currently available model-based methods use a common failure time model for all the patients in a treatment arm and predict the future failure times of the patients on study and patients yet to be enrolled. In the present work, we consider a scenario where the failure time depends on some important covariates at baseline such as gender, age, gene expression status and so on. We build a regression model introducing the covariates through the parameters of the failure time distributions. As our methods are based on predictive distributions of future failure times which are not available in closed form, we use Markov chain Monte Carlo (MCMC) methods to simulate from the predictive distribution. We demonstrate our methods with simulated data sets.

enrollment prediction

clinical trials

forecasting 

Biopharmaceutical Section