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Analysis of incomplete data using inverse probability weighting and doubly robust estimators

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Abstract
This article reviews inverse probability weighting methods and doubly robust estimation methods for the analysis of incomplete data sets. We first consider methods for estimating a population mean when the outcome is missing at random, in the sense that measured covariates can explain whether or not the outcome is observed. We then sketch the rationale of these methods and elaborate on their usefulness in the presence of influential inverse weights. We finally outline how to apply these methods in a variety of settings, such as for fitting regression models with incomplete outcomes or covariates, emphasizing the use of standard software programs.
Keywords
extrapolation, doubly robust estimation, extreme weights, Horvitz-Thompson estimator, inverse probability weighting, missing data, multiple imputation, MISSING DATA, MULTIPLE IMPUTATION, REGRESSION-MODELS, REPEATED OUTCOMES, INFERENCE, NONRESPONSE

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Chicago
Vansteelandt, Stijn, James Carpenter, and Michael G Kenward. 2010. “Analysis of Incomplete Data Using Inverse Probability Weighting and Doubly Robust Estimators.” Methodology-european Journal of Research Methods for the Behavioral and Social Sciences 6 (1): 37–48.
APA
Vansteelandt, S., Carpenter, J., & Kenward, M. G. (2010). Analysis of incomplete data using inverse probability weighting and doubly robust estimators. METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES, 6(1), 37–48. Presented at the Fall meeting of the Social Science Division of the Dutch Statistical Society.
Vancouver
1.
Vansteelandt S, Carpenter J, Kenward MG. Analysis of incomplete data using inverse probability weighting and doubly robust estimators. METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES. 2010;6(1):37–48.
MLA
Vansteelandt, Stijn, James Carpenter, and Michael G Kenward. “Analysis of Incomplete Data Using Inverse Probability Weighting and Doubly Robust Estimators.” METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES 6.1 (2010): 37–48. Print.
@article{1073694,
  abstract     = {This article reviews inverse probability weighting methods and doubly robust estimation methods for the analysis of incomplete data sets. We first consider methods for estimating a population mean when the outcome is missing at random, in the sense that measured covariates can explain whether or not the outcome is observed. We then sketch the rationale of these methods and elaborate on their usefulness in the presence of influential inverse weights. We finally outline how to apply these methods in a variety of settings, such as for fitting regression models with incomplete outcomes or covariates, emphasizing the use of standard software programs.},
  author       = {Vansteelandt, Stijn and Carpenter, James and Kenward, Michael G},
  issn         = {1614-1881},
  journal      = {METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES},
  keyword      = {extrapolation,doubly robust estimation,extreme weights,Horvitz-Thompson estimator,inverse probability weighting,missing data,multiple imputation,MISSING DATA,MULTIPLE IMPUTATION,REGRESSION-MODELS,REPEATED OUTCOMES,INFERENCE,NONRESPONSE},
  language     = {eng},
  location     = {Tilburg, The Netherlands},
  number       = {1},
  pages        = {37--48},
  title        = {Analysis of incomplete data using inverse probability weighting and doubly robust estimators},
  url          = {http://dx.doi.org/10.1027/1614-2241/a000005},
  volume       = {6},
  year         = {2010},
}

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