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

PSC In The News

RSS Feed icon

Frey and colleagues outline 10 trends showing scale of America's demographic transitions

Starr says surveys intended to predict recidivism assign higher risk to poor

Prescott and colleagues find incidence of noncompetes in U.S. labor force varies by job, state, worker education

Highlights

PAA 2015 Annual Meeting: Preliminary program and list of UM participants

ISR addition wins LEED Gold Certification

PSC Fall 2014 Newsletter now available

Martha Bailey and Nicolas Duquette win Cole Prize for article on War on Poverty

Next Brown Bag

Mon, March 9
Luigi Pistaferri, Consumption Inequality and Family Labor Supply

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