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Improving upon the efficiency of complete case analysis when covariates are MNAR

(2014) BIOSTATISTICS. 15(4). p.719-730
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Abstract
Missing values in covariates of regression models are a pervasive problem in empirical research. Popular approaches for analyzing partially observed datasets include complete case analysis (CCA), multiple imputation (MI), and inverse probability weighting (IPW). In the case of missing covariate values, these methods (as typically implemented) are valid under different missingness assumptions. In particular, CCA is valid under missing not at random (MNAR) mechanisms in which missingness in a covariate depends on the value of that covariate, but is conditionally independent of outcome. In this paper, we argue that in some settings such an assumption is more plausible than the missing at random assumption underpinning most implementations of MI and IPW. When the former assumption holds, although CCA gives consistent estimates, it does not make use of all observed information. We therefore propose an augmented CCA approach which makes the same conditional independence assumption for missingness as CCA, but which improves efficiency through specification of an additional model for the probability of missingness, given the fully observed variables. The new method is evaluated using simulations and illustrated through application to data on reported alcohol consumption and blood pressure from the US National Health and Nutrition Examination Survey, in which data are likely MNAR independent of outcome.
Keywords
Missing not at random, Multiple imputation, Missing covariates, Complete case analysis, MISSING DATA, MULTIPLE IMPUTATION, REGRESSION, MODELS, NONRESPONSE

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Please use this url to cite or link to this publication:

MLA
Bartlett, Jonathan W, James R Carpenter, Kate Tilling, et al. “Improving Upon the Efficiency of Complete Case Analysis When Covariates Are MNAR.” BIOSTATISTICS 15.4 (2014): 719–730. Print.
APA
Bartlett, J. W., Carpenter, J. R., Tilling, K., & Vansteelandt, S. (2014). Improving upon the efficiency of complete case analysis when covariates are MNAR. BIOSTATISTICS, 15(4), 719–730.
Chicago author-date
Bartlett, Jonathan W, James R Carpenter, Kate Tilling, and Stijn Vansteelandt. 2014. “Improving Upon the Efficiency of Complete Case Analysis When Covariates Are MNAR.” Biostatistics 15 (4): 719–730.
Chicago author-date (all authors)
Bartlett, Jonathan W, James R Carpenter, Kate Tilling, and Stijn Vansteelandt. 2014. “Improving Upon the Efficiency of Complete Case Analysis When Covariates Are MNAR.” Biostatistics 15 (4): 719–730.
Vancouver
1.
Bartlett JW, Carpenter JR, Tilling K, Vansteelandt S. Improving upon the efficiency of complete case analysis when covariates are MNAR. BIOSTATISTICS. 2014;15(4):719–30.
IEEE
[1]
J. W. Bartlett, J. R. Carpenter, K. Tilling, and S. Vansteelandt, “Improving upon the efficiency of complete case analysis when covariates are MNAR,” BIOSTATISTICS, vol. 15, no. 4, pp. 719–730, 2014.
@article{5765135,
  abstract     = {Missing values in covariates of regression models are a pervasive problem in empirical research. Popular approaches for analyzing partially observed datasets include complete case analysis (CCA), multiple imputation (MI), and inverse probability weighting (IPW). In the case of missing covariate values, these methods (as typically implemented) are valid under different missingness assumptions. In particular, CCA is valid under missing not at random (MNAR) mechanisms in which missingness in a covariate depends on the value of that covariate, but is conditionally independent of outcome. In this paper, we argue that in some settings such an assumption is more plausible than the missing at random assumption underpinning most implementations of MI and IPW. When the former assumption holds, although CCA gives consistent estimates, it does not make use of all observed information. We therefore propose an augmented CCA approach which makes the same conditional independence assumption for missingness as CCA, but which improves efficiency through specification of an additional model for the probability of missingness, given the fully observed variables. The new method is evaluated using simulations and illustrated through application to data on reported alcohol consumption and blood pressure from the US National Health and Nutrition Examination Survey, in which data are likely MNAR independent of outcome.},
  author       = {Bartlett, Jonathan W and Carpenter, James R and Tilling, Kate and Vansteelandt, Stijn},
  issn         = {1465-4644},
  journal      = {BIOSTATISTICS},
  keywords     = {Missing not at random,Multiple imputation,Missing covariates,Complete case analysis,MISSING DATA,MULTIPLE IMPUTATION,REGRESSION,MODELS,NONRESPONSE},
  language     = {eng},
  number       = {4},
  pages        = {719--730},
  title        = {Improving upon the efficiency of complete case analysis when covariates are MNAR},
  url          = {http://dx.doi.org/10.1093/biostatistics/kxu023},
  volume       = {15},
  year         = {2014},
}

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