Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Lam says tightening global labor market good for American workers

Johnston says e-cigs may reverse two-decades of progress on smoking reduction

Mueller-Smith finds incarceration increases the likelihood of committing more, and more serious, crimes

Highlights

Bob Willis awarded 2015 Jacob Mincer Award for Lifetime Contributions to the Field of Labor Economics

David Lam is new director of Institute for Social Research

Elizabeth Bruch wins Robert Merton Prize for paper in analytic sociology

Elizabeth Bruch wins ASA award for paper in mathematical sociology

Next Brown Bag

PSC Brown Bags will be back fall 2015


Predicting event. times in clinical trials when randomization is masked and blocked

Publication Abstract

Donovan, J.M., Michael R. Elliott, and D.F. Heitjan. 2007. "Predicting event. times in clinical trials when randomization is masked and blocked." Clinical Trials, 4(5): 481-490.

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.

Browse | Search : All Pubs | Next