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

PSC In The News

RSS Feed icon

Stephenson assessing in-home HIV testing and counseling for male couples

Thompson says mass incarceration causes collapse of Detroit neighborhoods

Liberal-conservative gap by education level growing in U.S.

Highlights

Maggie Levenstein named director of ISR's Inter-university Consortium for Political and Social Research

Arline Geronimus receives 2016 Harold R. Johnson Diversity Service Award

PSC spring 2016 newsletter: Kristin Seefeldt, Brady West, newly funded projects, ISR Runs for Bob, and more

AAUP reports on faculty compensation by category, affiliation, and academic rank

Next Brown Bag

PSC Brown Bags
will resume fall 2016

Predicting event times in clinical trials when treatment arm is masked

Publication Abstract

Donovan, J.M., Michael R. Elliott, and D.F. Heitjan. 2006. "Predicting event times in clinical trials when treatment arm is masked." Journal of Biopharmaceutical Statistics, 16(3): 343-356.

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.

DOI:10.1080/10543400600609445 (Full Text)

Browse | Search : All Pubs | Next