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March 2012 Two-pronged Strategy for Using DIC to Compare Selection Models with Non-Ignorable Missing Responses
Nicky Best, Alexina Mason, Sylvia Richardson
Bayesian Anal. 7(1): 109-146 (March 2012). DOI: 10.1214/12-BA704

Abstract

Data with missing responses generated by a non-ignorable missing- ness mechanism can be analysed by jointly modelling the response and a binary variable indicating whether the response is observed or missing. Using a selection model factorisation, the resulting joint model consists of a model of interest and a model of missingness. In the case of non-ignorable missingness, model choice is difficult because the assumptions about the missingness model are never verifiable from the data at hand. For complete data, the Deviance Information Criterion (DIC) is routinely used for Bayesian model comparison. However, when an analysis includes missing data, DIC can be constructed in different ways and its use and interpretation are not straightforward. In this paper, we present a strategy for comparing selection models by combining information from two measures taken from different constructions of the DIC. A DIC based on the observed data likelihood is used to compare joint models with different models of interest but the same model of missingness, and a comparison of models with the same model of interest but different models of missingness is carried out using the model of missingness part of a conditional DIC. This strategy is intended for use within a sensitivity analysis that explores the impact of different assumptions about the two parts of the model, and is illustrated by examples with simulated missingness and an application which compares three treatments for depression using data from a clinical trial. We also examine issues relating to the calculation of the DIC based on the observed data likelihood.

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Nicky Best. Alexina Mason. Sylvia Richardson. "Two-pronged Strategy for Using DIC to Compare Selection Models with Non-Ignorable Missing Responses." Bayesian Anal. 7 (1) 109 - 146, March 2012. https://doi.org/10.1214/12-BA704

Information

Published: March 2012
First available in Project Euclid: 13 June 2012

zbMATH: 1330.62092
MathSciNet: MR2896714
Digital Object Identifier: 10.1214/12-BA704

Rights: Copyright © 2012 International Society for Bayesian Analysis

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Vol.7 • No. 1 • March 2012
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