PMID: 22347786Feb 22, 2012Paper

Doubly Robust Nonparametric Multiple Imputation for Ignorable Missing Data.

Statistica Sinica
Qi LongYisheng Li

Abstract

Missing data are common in medical and social science studies and often pose a serious challenge in data analysis. Multiple imputation methods are popular and natural tools for handling missing data, replacing each missing value with a set of plausible values that represent the uncertainty about the underlying values. We consider a case of missing at random (MAR) and investigate the estimation of the marginal mean of an outcome variable in the presence of missing values when a set of fully observed covariates is available. We propose a new nonparametric multiple imputation (MI) approach that uses two working models to achieve dimension reduction and define the imputing sets for the missing observations. Compared with existing nonparametric imputation procedures, our approach can better handle covariates of high dimension, and is doubly robust in the sense that the resulting estimator remains consistent if either of the working models is correctly specified. Compared with existing doubly robust methods, our nonparametric MI approach is more robust to the misspecification of both working models; it also avoids the use of inverse-weighting and hence is less sensitive to missing probabilities that are close to 1. We propose a sensi...Continue Reading

Citations

Dec 10, 2015·Biometrical Journal. Biometrische Zeitschrift·Chiu-Hsieh HsuRandall Friese
Dec 5, 2017·Journal of the American Medical Informatics Association : JAMIA·Yuan LuoJason M Baron

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