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A method for increasing the robustness of multiple imputation

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  • Daniel, Rhian M.
  • Kenward, Michael G.

Abstract

Missing data are common wherever statistical methods are applied in practice. They present a problem in that they require that additional assumptions be made about the mechanism leading to the incompleteness of the data. By incorporating two models for the missing data process, doubly robust (DR) weighting-based methods offer some protection against misspecification bias since inferences are valid when at least one of the two models is correctly specified. The balance between robustness, efficiency and analytical complexity is one which is difficult to strike, resulting in a split between the likelihood and multiple imputation (MI) school on one hand and the weighting and DR school on the other. An extension of MI is proposed that, in certain settings, can be shown to give rise to DR estimators. It is conjectured that this additional robustness holds more generally, as demonstrated using simulation studies. The method is applied to data from the RECORD study, a clinical trial comparing anti-glycaemic combination therapies in type II diabetes patients.

Suggested Citation

  • Daniel, Rhian M. & Kenward, Michael G., 2012. "A method for increasing the robustness of multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1624-1643.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:6:p:1624-1643
    DOI: 10.1016/j.csda.2011.10.006
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    References listed on IDEAS

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    1. Anastasios A. Tsiatis & Marie Davidian & Weihua Cao, 2011. "Improved Doubly Robust Estimation When Data Are Monotonely Coarsened, with Application to Longitudinal Studies with Dropout," Biometrics, The International Biometric Society, vol. 67(2), pages 536-545, June.
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    3. James R. Carpenter & Michael G. Kenward & Stijn Vansteelandt, 2006. "A comparison of multiple imputation and doubly robust estimation for analyses with missing data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 571-584, July.
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    6. Creemers, An & Aerts, Marc & Hens, Niel & Molenberghs, Geert, 2012. "A nonparametric approach to weighted estimating equations for regression analysis with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 100-113, January.
    7. Patrick Royston, 2004. "Multiple imputation of missing values," Stata Journal, StataCorp LP, vol. 4(3), pages 227-241, September.
    8. Zhiqiang Tan, 2010. "Bounded, efficient and doubly robust estimation with inverse weighting," Biometrika, Biometrika Trust, vol. 97(3), pages 661-682.
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    Cited by:

    1. A.Y. Kombo & H. Mwambi & G. Molenberghs, 2017. "Multiple imputation for ordinal longitudinal data with monotone missing data patterns," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 270-287, January.
    2. Zhang, Mimi & Hu, Qingpei & Xie, Min & Yu, Dan, 2014. "Lower confidence limit for reliability based on grouped data using a quantile-filling algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 96-111.
    3. Manuel Gomes & Nils Gutacker & Chris Bojke & Andrew Street, 2016. "Addressing Missing Data in Patient‐Reported Outcome Measures (PROMS): Implications for the Use of PROMS for Comparing Provider Performance," Health Economics, John Wiley & Sons, Ltd., vol. 25(5), pages 515-528, May.
    4. Manuel Gomes & Nils Gutacker & Chris Bojke & Andrew Street, 2014. "Addressing missing data in patient-reported outcome measures (PROMs): implications for comparing provider performance," Working Papers 101cherp, Centre for Health Economics, University of York.

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