Advanced search
1 file | 220.21 KB

Introduction to double robust methods for incomplete data

(2018) STATISTICAL SCIENCE. 33(2). p.184-197
Author
Organization
Abstract
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.
Keywords
Augmented inverse probability weighting, calibration estimators, data-adaptive methods, doubly robust, empirical likelihood, imputation, inverse probability weighting, missing data, semiparametric methods, GENERALIZED ESTIMATING EQUATIONS, CAUSAL INFERENCE MODELS, MISSING DATA, NONRESPONSE MODELS, LONGITUDINAL DATA, REGRESSION, ESTIMATORS, SELECTION, BIAS, EFFICIENT

Downloads

  • drpaper revised.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 220.21 KB

Citation

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

Chicago
Seaman, Shaun R, and Stijn Vansteelandt. 2018. “Introduction to Double Robust Methods for Incomplete Data.” Statistical Science.
APA
Seaman, S. R., & Vansteelandt, S. (2018). Introduction to double robust methods for incomplete data. STATISTICAL SCIENCE.
Vancouver
1.
Seaman SR, Vansteelandt S. Introduction to double robust methods for incomplete data. STATISTICAL SCIENCE. 2018. p. 184–97.
MLA
Seaman, Shaun R, and Stijn Vansteelandt. “Introduction to Double Robust Methods for Incomplete Data.” STATISTICAL SCIENCE 2018 : 184–197. Print.
@misc{8567085,
  abstract     = {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 {\textacutedbl}design-consistent{\textacutedbl} estimators used in sample surveys, and explain the value of double robustness when using flexible data-adaptive methods for IPW or imputation.},
  author       = {Seaman, Shaun R and Vansteelandt, Stijn},
  issn         = {0883-4237},
  keyword      = {Augmented inverse probability weighting,calibration estimators,data-adaptive methods,doubly robust,empirical likelihood,imputation,inverse probability weighting,missing data,semiparametric methods,GENERALIZED ESTIMATING EQUATIONS,CAUSAL INFERENCE MODELS,MISSING DATA,NONRESPONSE MODELS,LONGITUDINAL DATA,REGRESSION,ESTIMATORS,SELECTION,BIAS,EFFICIENT},
  language     = {eng},
  number       = {2},
  pages        = {184--197},
  series       = {STATISTICAL SCIENCE},
  title        = {Introduction to double robust methods for incomplete data},
  url          = {http://dx.doi.org/10.1214/18-sts647},
  volume       = {33},
  year         = {2018},
}

Altmetric
View in Altmetric
Web of Science
Times cited: