Multiple imputation under Bayesianly smoothed pattern-mixture models for non-ignorable drop-out.
Conventional pattern-mixture models can be highly sensitive to model misspecification. In many longitudinal studies, where the nature of the drop-out and the form of the population model are unknown, interval estimates from any single pattern-mixture model may suffer from undercoverage, because uncertainty about model misspecification is not taken into account. In this article, a new class of Bayesian random coefficient pattern-mixture models is developed to address potentially non-ignorable drop-out. Instead of imposing hard equality constraints to overcome inherent inestimability problems in pattern-mixture models, we propose to smooth the polynomial coefficient estimates across patterns using a hierarchical Bayesian model that allows random variation across groups. Using real and simulated data, we show that multiple imputation under a three-level linear mixed-effects model which accommodates a random level due to drop-out groups can be an effective method to deal with non-ignorable drop-out by allowing model uncertainty to be incorporated into the imputation process.
Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL 60612-4336, USA. demirtas@uic.edu
Hakan
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Demirtas
D014150:Antipsychotic Agents / Q000627:therapeutic use; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D010352:Patient Dropouts; D016032:Randomized Controlled Trials as Topic; D012559:Schizophrenia / Q000188:drug therapy
D014150:Antipsychotic Agents