Journal article
Posterior predictive checking of multiple imputation models
CD Nguyen, KJ Lee, JB Carlin
Biometrical Journal | Published : 2015
Abstract
Multiple imputation is gaining popularity as a strategy for handling missing data, but there is a scarcity of tools for checking imputation models, a critical step in model fitting. Posterior predictive checking (PPC) has been recommended as an imputation diagnostic. PPC involves simulating "replicated" data from the posterior predictive distribution of the model under scrutiny. Model fit is assessed by examining whether the analysis from the observed data appears typical of results obtained from the replicates produced by the model. A proposed diagnostic measure is the posterior predictive "p-value", an extreme value of which (i.e., a value close to 0 or 1) suggests a misfit between the mod..
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Awarded by National Health and Medical Research Council
Awarded by National Health and Medical Research Council: Centre of Research Excellence
Funding Acknowledgements
This work was supported by funding from the National Health and Medical Research Council: Career Development Fellowship ID 1053609 (KJL), a Centre of Research Excellence grant ID 1035261 (JBC), awarded to the Victorian Centre for Biostatistics (ViCBiostat), and Project Grant ID 607400 (JBC, KJL). The authors would like to thank Ian White for helpful discussions relating to this paper. The authors also acknowledge support provided to the Murdoch Childrens Research Institute through the Victorian Government's Operational Infrastructure Support Program. This paper used unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership among the Department of Social Services (DSS), Australian Institute of Family Studies (AIFS), and Australian Bureau of Statistics (ABS). The findings and views reported in this paper are those of the authors and should not be attributed to DSS, AIFS, or ABS.