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

PSC In The News

RSS Feed icon

Surprising findings on what influences unintended pregnancy from Wise, Geronimus and Smock

Recommendations on how to reduce discrimination resulting from ban-the-box policies cite Starr's work

Brian Jacob on NAEP scores: "Michigan is the only state in the country where proficiency rates have actually declined over time."

More News

Highlights

Call for papers: Conference on computational social science, April 2017, U-M

Sioban Harlow honored with 2017 Sarah Goddard Power Award for commitment to women's health

Post-doc fellowship in computational social science for summer or fall 2017, U-Penn

ICPSR Summer Program scholarships to support training in statistics, quantitative methods, research design, and data analysis

More Highlights

Next Brown Bag

Mon, March 13, 2017, noon:
Rachel Best

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