This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 501
Type: Topic Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #306436
Title: Analysis and Sensitivity Analysis for Incomplete Data
Author(s): Geert Molenberghs*+
Companies: I-BioStat
Address: Center for Statistics, Diepenbeek, International, B3590, Belgium
Keywords: missing data ; latent variable ; sensitivity
Abstract:

Many missing-data models exist, often allowing for MNAR. The sensitivity to unverifiable modeling assumptions has led to sensitivity-analysis research. The nature of sensitivity originates from the fact that an MNAR model is not fully verifiable from data, rendering empirical distinction between MNAR and MAR hard or even impossible, unless one is prepared to accept the posited MNAR model in an unquestioning way. In this paper, we show that the empirical distinction between MAR and MNAR is not possible, in the sense that each MNAR model fit to a set of observed data can be reproduced exactly by an MAR counterpart. Such a pair of models will produce different predictions of the unobserved outcomes, given the observed ones. Theoretical considerations are supplemented with data analysis. Similar issues arising in grouped and censored data, and models with latent structures, are presented.


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