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

PSC In The News

RSS Feed icon

Work by Geronimus cited in account of Serena Williams' maternal health complications

Alexander and Massey compare outcomes for children whose parents did and did not take part in Great Migration

Geronimus on pushing past early dismissal of her weathering hypothesis

More News

Highlights

Robert Wood Johnson Foundation health leadership development programs accepting applications

AA named 2018 Best Place to Live in America (out of 100 cities)

Remembering Jim Morgan, founding member of ISR and creator of the PSID

1/17/18: ISR screening and discussion of documentary "Class Divide" at Michigan Theater

More Highlights

Next Brown Bag

Mon, Jan 22, 2018, noon: Narayan Sastry

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