Semi-parametric Bayesian analysis of binary responses with a continuous covariate subject to non-random missingness. (February 2015)

• Record Type: Journal Article
• Authors: Poleto, Frederico Z; Paulino, Carlos Daniel; Singer, Julio M; Molenberghs, Geert
• Is Part Of: Statistical modelling. Volume 15:Number 1(2015)
• Page Start: 1
• Page End: 23
• Publication Date: 2015-02
• Subjects: Dirichlet process mixture -- incomplete data -- non-ignorable missingness mechanism -- missing not at random -- MNAR; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X14549290
• Languages: English
• ISSNs: 1471-082X

Abstract

Missingness in explanatory variables requires a model for the covariates even if the interest lies only in a model for the outcomes given the covariates. An incorrect specification of the models for the covariates or for the missingness mechanism may lead to biased inferences for the parameters of interest. Previously published articles either use semi-/non-parametric flexible distributions for the covariates and identify the model via a missing at random assumption, or employ parametric distributions for the covariates and allow a more general non-random missingness mechanism. We consider the analysis of binary responses, combining a missing not at random mechanism with a non-parametric model based on a Dirichlet process mixture for the continuous covariates. We illustrate the proposal with simulations and the analysis of a dataset.