ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2018
Introduction to Double Robust Methods for Incomplete Data.
Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Longer-term chiropractic care outcomes for US active-duty military personnel with low back pain: secondary analysis of a pragmatic clinical trial.BMC complementary medicine and therapies · 2026Trial
- Causal Effect Estimation With TMLE: Handling Missing Data and Near Violations of Positivity.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Article
- Analyzing Left-Truncated Samples with the Cox Model in the Presence of Missing Covariates.Statistics in biosciences · 2025Article
- A framework for understanding selection bias in real-world healthcare data.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Highly robust causal semiparametric U-statistic with applications in biomedical studies.The international journal of biostatistics · 2024Article
- Quantifying bias due to missing data in quality of life surveys of advanced-stage cancer patients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024Article
- Enhanced doubly robust estimation with concave link functions for estimands in clinical trials.Journal of nonparametric statistics · 2024Article
- Model misspecification and bias for inverse probability weighting estimators of average causal effects.Biometrical journal. Biometrische Zeitschrift · 2023Article
- AIPW: An R Package for Augmented Inverse Probability-Weighted Estimation of Average Causal Effects.American journal of epidemiology · 2021Article
- Intergenerational effects of violence on women's perinatal wellbeing and infant health outcomes: evidence from a birth cohort study in Central Vietnam.BMC pregnancy and childbirth · 2021Article
- 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.The annals of applied statistics · 2021Article
- Introduction to Double Robust Methods for Incomplete Data.Statistical science : a review journal of the Institute of Mathematical Statistics · 2018Article
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2 authors.
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Abstract
Most methods for handling incomplete data can be broadly classified as inverse probability weighting (IPW) strategies or imputation strategies. The former model the occurrence of incomplete data; the latter, the distribution of the missing variables given observed variables in each missingness pattern. Imputation strategies are typically more efficient, but they can involve extrapolation, which is difficult to diagnose and can lead to large bias. Double robust (DR) methods combine the two approaches. They are typically more efficient than IPW and more robust to model misspecification than imputation. We give a formal introduction to DR estimation of the mean of a partially observed variable, before moving to more general incomplete-data scenarios. We review strategies to improve the performance of DR estimators under model misspecification, reveal connections between DR estimators for incomplete data and 'design-consistent' estimators used in sample surveys, and explain the value of double robustness when using flexible data-adaptive methods for IPW or imputation.
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