title: Multiple imputation for model checking: completed-data plots with missing and latent data.
abstract: In problems with missing or latent data, a standard approach is to first impute the unobserved data, then perform all statistical analyses on the completed dataset--corresponding to the observed data and imputed unobserved data--using standard procedures for complete-data inference. Here, we extend this approach to model checking by demonstrating the advantages of the use of completed-data model diagnostics on imputed completed datasets. The approach is set in the theoretical framework of Bayesian posterior predictive checks (but, as with missing-data imputation, our methods of missing-data model checking can also be interpreted as "predictive inference" in a non-Bayesian context). We consider the graphical diagnostics within this framework. Advantages of the completed-data approach include: (1) One can often check model fit in terms of quantities that are of key substantive interest in a natural way, which is not always possible using observed data alone. (2) In problems with missing data, checks may be devised that do not require to model the missingness or inclusion mechanism; the latter is useful for the analysis of ignorable but unknown data collection mechanisms, such as are often assumed in the analysis of sample surveys and observational studies. (3) In many problems with latent data, it is possible to check qualitative features of the model (for example, independence of two variables) that can be naturally formalized with the help of the latent data. We illustrate with several applied examples.
authors.affiliation: Department of Statistics, Columbia University, New York 10027, USA. gelman@stat.columbia.edu
authors.forename: Andrew
authors.initials: A
authors.lastname: Gelman
authors.forename: Iven
authors.initials: I
authors.lastname: Van Mechelen
authors.forename: Geert
authors.initials: G
authors.lastname: Verbeke
authors.forename: Daniel F
authors.initials: DF
authors.lastname: Heitjan
authors.forename: Michel
authors.initials: M
authors.lastname: Meulders
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003625:Data Collection; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D010352:Patient Dropouts / Q000706:statistics & numerical data; D051381:Rats