title: Diagnosing imputation models by applying target analyses to posterior replicates of completed data.
abstract: Multiple imputation fills in missing data with posterior predictive draws from imputation models. To assess the adequacy of imputation models, we can compare completed data with their replicates simulated under the imputation model. We apply analyses of substantive interest to both datasets and use posterior predictive checks of the differences of these estimates to quantify the evidence of model inadequacy. We can further integrate out the imputed missing data and their replicates over the completed-data analyses to reduce variance in the comparison. In many cases, the checking procedure can be easily implemented using standard imputation software by treating re-imputations under the model as posterior predictive replicates. Thus, it can be applied for non-Bayesian imputation methods. We also sketch several strategies for applying the method in the context of practical imputation analyses. We illustrate the method using two real data applications and study its property using a simulation.
authors.affiliation: Department of Health Care Policy, Harvard Medical School, Boston, MA 02115, USA. he@hcp.med.harvard.edu
authors.forename: Yulei
authors.initials: Y
authors.lastname: He
authors.forename: Alan M
authors.initials: AM
authors.lastname: Zaslavsky
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009101:Multiple Myeloma / Q000188:drug therapy; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D017384:Sequence Deletion; D016019:Survival Analysis
chemical_list: D000970:Antineoplastic Agents