Diagnosing Imputation Models by Applying Target Analyses Under Posterior Replications of Observed Data

• Activity Number: 9
• Type: Invited
• Date/Time: Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Yulei He and Alan Zaslavsky*+
• Companies: Harvard Medical School and Harvard Medical School

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 to 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 sketch several strategies for applying the method in the context of practical imputation analyses.