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2018 ; 33
(2
): 184-197
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Introduction to Double Robust Methods for Incomplete Data
#MMPMID29731541
Seaman SR
; Vansteelandt S
Stat Sci
2018[]; 33
(2
): 184-197
PMID29731541
show ga
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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