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2016 ; 58
(3
): 588-606
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Doubly robust multiple imputation using kernel-based techniques
#MMPMID26647734
Hsu CH
; He Y
; Li Y
; Long Q
; Friese R
Biom J
2016[May]; 58
(3
): 588-606
PMID26647734
show ga
We consider the problem of estimating the marginal mean of an incompletely
observed variable and develop a multiple imputation approach. Using fully
observed predictors, we first establish two working models: one predicts the
missing outcome variable, and the other predicts the probability of missingness.
The predictive scores from the two models are used to measure the similarity
between the incomplete and observed cases. Based on the predictive scores, we
construct a set of kernel weights for the observed cases, with higher weights
indicating more similarity. Missing data are imputed by sampling from the
observed cases with probability proportional to their kernel weights. The
proposed approach can produce reasonable estimates for the marginal mean and has
a double robustness property, provided that one of the two working models is
correctly specified. It also shows some robustness against misspecification of
both models. We demonstrate these patterns in a simulation study. In a real-data
example, we analyze the total helicopter response time from injury in the Arizona
emergency medical service data.
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