Robust inference when combining inverse-probability weighting and multiple imputation to address missing data with application to an electronic health records-based study of bariatric surgery

DOI10.1214/20-AOAS1386zbMath1475.62267OpenAlexW3137602326MaRDI QIDQ2233145

Tanayott Thaweethai, Sebastien Haneuse, David E. Arterburn, Karen J. Coleman

Publication date: 14 October 2021

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/20-aoas1386

zbMATH Keywords

missing datamultiple imputationmodel misspecificationinverse-probability weightingobesitychronic kidney diseaseelectronic health recordsbariatric surgery

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Missing data (62D10)

Uses Software

• MICE

Cites Work

• Unnamed Item
• Doubly Robust Estimation in Missing Data and Causal Inference Models
• Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
• Asymptotic results for multiple imputation
• Introduction to double robust methods for incomplete data
• Combining Multiple Imputation and Inverse‐Probability Weighting
• On the Bias of the Multiple-Imputation Variance Estimator in Survey Sampling
• Inference and missing data
• Large-sample theory for parametric multiple imputation procedures
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• On the multiple imputation variance estimator for control‐based and delta‐adjusted pattern mixture models
• Proper and Improper Multiple Imputation
• Inference for imputation estimators
• Inference for Misspecified Models With Fixed Regressors
• Multiple imputation: current perspectives
• Bias Introduced by Rounding in Multiple Imputation for Ordered Categorical Variables
• Maximum Likelihood Estimation of Misspecified Models