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COSSA makes 10 suggestions to next Administration for supporting and using social science research

Thompson says US prison population is 'staggeringly high' at about 1.5 million, despite 2% drop for 2015

Levy et al. find Michigan's Medicaid expansion boosted state's economy while increasing number of insured

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2017 PAA Annual Meeting, April 27-29, Chicago

NIH funding opportunity: Etiology of Health Disparities and Health Advantages among Immigrant Populations (R01 and R21), open Jan 2017

Russell Sage 2017 Summer Institute in Computational Social Science, June 18-July 1. Application deadline Feb 17.

Russell Sage 2-week workshop on social science genomics, June 11-23, 2017, Santa Barbara

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Mon, Jan 23, 2017 at noon:
Decline of cash assistance and child well-being, Luke Shaefer

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.

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