Predicting event times in clinical trials when treatment arm is masked.
Because power is primarily determined by the number of events in event-based clinical trials, the timing for interim or final analysis of data is often determined based on the accrual of events during the course of the study. Thus, it is of interest to predict early and accurately the time of a landmark interim or terminating event. Existing Bayesian methods may be used to predict the date of the landmark event, based on current enrollment, event, and loss to follow-up, if treatment arms are known. This work extends these methods to the case where the treatment arms are masked by using a parametric mixture model with a known mixture proportion. Posterior simulation using the mixture model is compared with methods assuming a single population. Comparison of the mixture model with the single-population approach shows that with few events, these approaches produce substantially different results and that these results converge as the prediction time is closer to the landmark event. Simulations show that the mixture model with diffuse priors can have better coverage probabilities for the prediction interval than the nonmixture models if a treatment effect is present.
Donovan
J Mark
JM
Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia 19104-6021, USA. jdonovan@cceb.upenn.edu
Elliott
Michael R
MR
Heitjan
Daniel F
DF
D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006105:Granulomatous Disease, Chronic / Q000188:drug therapy; D006801:Humans; D007371:Interferon-gamma / Q000627:therapeutic use; D016013:Likelihood Functions; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D012107:Research Design; D018401:Sample Size
D007371:Interferon-gamma