Multiple Imputation for Model Checking: Completed-Data Plots with Missing and Latent Data

• Published in: Biometrics Vol. 61; no. 1; pp. 74 - 85
• Main Authors: Gelman, Andrew, Van Mechelen, Iven, Verbeke, Geert, Heitjan, Daniel F., Meulders, Michel
• Format: Journal Article
• Language: English
• Published: 350 Main Street , Malden , MA 02148 , U.S.A , and P.O. Box 1354, 9600 Garsington Road , Oxford OX4 2DQ , U.K Blackwell Publishing 01.03.2005; International Biometric Society; Blackwell Publishing Ltd
• Subjects: Animals; Bayes Theorem; Bayesian model checking; Clinical Trials as Topic - statistics & numerical data; Data Collection; Data imputation; Data Interpretation, Statistical; Data models; Datasets; Dosage; Exploratory data analysis; Histograms; Humans; Missing data; Modeling; Models, Statistical; Multiple imputation; Nonresponse; Patient Dropouts - statistics & numerical data; Posterior predictive checks; Predictive modeling; Rats; Realized discrepancies; Residuals; School dropouts; Symptoms

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 th...