Statistical Science

Introduction to Double Robust Methods for Incomplete Data

Shaun R. Seaman and Stijn Vansteelandt

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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.

Article information

Source
Statist. Sci., Volume 33, Number 2 (2018), 184-197.

Dates
First available in Project Euclid: 3 May 2018

Permanent link to this document
https://projecteuclid.org/euclid.ss/1525313141

Digital Object Identifier
doi:10.1214/18-STS647

Mathematical Reviews number (MathSciNet)
MR3797709

Zentralblatt MATH identifier
1397.62176

Keywords
Augmented inverse probability weighting calibration estimators data-adaptive methods doubly robust empirical likelihood imputation inverse probability weighting missing data semiparametric methods

Citation

Seaman, Shaun R.; Vansteelandt, Stijn. Introduction to Double Robust Methods for Incomplete Data. Statist. Sci. 33 (2018), no. 2, 184--197. doi:10.1214/18-STS647. https://projecteuclid.org/euclid.ss/1525313141


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Supplemental materials

  • Supplement to “Introduction to double robust methods for incomplete data”. Additional semi-parametric theory, proofs, connections between methods, empirical likelihood DR estimators and software.