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

PSC In The News

RSS Feed icon

Work by Bailey and Dynarski cited in NYT piece on income inequality

Pfeffer says housing bubble masked decade-long growth in household net worth inequality

House, Burgard, Schoeni et al find that unemployment and recession have contrasting effects on mortality risk

Highlights

Jeff Morenoff makes Reuters' Highly Cited Researchers list for 2014

Susan Murphy named Distinguished University Professor

Sarah Burgard and former PSC trainee Jennifer Ailshire win ASA award for paper

James Jackson to be appointed to NSF's National Science Board

Next Brown Bag


PSC Brown Bags will return in the fall

A Bayesian sensitivity model for intention-to-treat analysis on binary outcomes with dropouts

Publication Abstract

Kaciroti, N.A., M.A. Schork, Trivellore Raghunathan, and S. Julius. 2009. "A Bayesian sensitivity model for intention-to-treat analysis on binary outcomes with dropouts." Statistics in Medicine, 28(4): 572-585.

Intention-to-treat (ITT) analysis is commonly used in randomized clinical trials. However, the use of ITT analysis presents a challenge: how to deal with Subjects who drop Out. Here we focus oil randomized trials where the primary outcome is a binary endpoint. Several approaches are available for including the dropout subject ill the ITT analysis, mainly chosen prior to unblinding the Study. These approaches reduce the potential bias due to breaking the randomization code.. However, the validity of the results will highly depend oil untestable assumptions; about the dropout mechanism. Thus, it is important to evaluate the sensitivity of the results across different missing-data mechanisms. We propose here a Bayesian pattern-mixture model for ITT analysis of binary outcomes with dropouts that applies over different types of missing-data mechanisms. We introduce it new parameterization to identify the model, which is then used for sensitivity analysis. The parameterization is defined as the odds ratio of having all endpoint between the Subjects who dropped Out and those who completed the study. Such parameterization is intuitive and easy 10 use ill sensitivity analysis; it also incorporates most of the available methods as special cases. The model is applied to TRial Of Preventing HYpertension. Copyright (C) 2008 John Wiley & Sons. Ltd.

DOI:10.1002/sim.3494 (Full Text)

Browse | Search : All Pubs | Next