Combining Multiple Imputation and Inverse‐Probability Weighting

DOI10.1111/J.1541-0420.2011.01666.XzbMath1241.62009OpenAlexW1972264001WikidataQ36141631 ScholiaQ36141631MaRDI QIDQ2893996

Andrew Copas, Leah Li, Ian R. White, Shaun R. Seaman

Publication date: 27 June 2012

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2011.01666.x

zbMATH Keywords

missing at randommarginal modelsurvey weighting1958 British Birth Cohort

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Sampling theory, sample surveys (62D05)

Related Items (10)

A weighted simulation-based estimator for incomplete longitudinal data models ⋮ Inverse Probability Weighting with Missing Predictors of Treatment Assignment or Missingness ⋮ Nonparametric Inverse-Probability-Weighted Estimators Based on the Highly Adaptive Lasso ⋮ Multiple imputation methods for handling missing values in longitudinal studies with sampling weights: Comparison of methods implemented in Stata ⋮ Missing data: A statistical framework for practice ⋮ Nonresponse adjusted estimation based on a composite weighting method in a panel survey ⋮ Systematic handling of missing data in complex study designs – experiences from the Health 2000 and 2011 Surveys ⋮ 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 ⋮ A two-step semiparametric method to accommodate sampling weights in multiple imputation ⋮ Strategies for handling missing data in longitudinal studies with questionnaires

Uses Software

• ice

Cites Work

• Asymptotic results for multiple imputation
• Indicator and Stratification Methods for Missing Explanatory Variables in Multiple Linear Regression
• On the Bias of the Multiple-Imputation Variance Estimator in Survey Sampling
• Large-sample theory for parametric multiple imputation procedures
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Proper and Improper Multiple Imputation
• Inference for imputation estimators
• Multiple imputation of discrete and continuous data by fully conditional specification
• Unnamed Item
• Unnamed Item

This page was built for publication: Combining Multiple Imputation and Inverse‐Probability Weighting