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A weighted simulation-based estimator for incomplete longitudinal data models

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  • Li, Daniel H.
  • Wang, Liqun

Abstract

Recently, Li and Wang (2012a,b) and Wang (2007) have proposed a simulation-based estimator for generalized linear and nonlinear mixed models with complete longitudinal data. This estimator is constructed using the simulation-by-parts technique which leads to the unique feature that it is consistent even using finite number of simulated random points. This paper extends the methodology to deal with incomplete longitudinal data by applying the inverse probability weighting method for the monotone missing-at-random response data. The finite sample performance of this estimator is investigated through simulation studies and compared with the multiple imputation approach.

Suggested Citation

  • Li, Daniel H. & Wang, Liqun, 2016. "A weighted simulation-based estimator for incomplete longitudinal data models," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 16-22.
    • Handle: RePEc:eee:stapro:v:113:y:2016:i:c:p:16-22
    DOI: 10.1016/j.spl.2016.02.004
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    References listed on IDEAS

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    1. 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.
    2. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.
    3. Vonesh E. F. & Wang H. & Nie L. & Majumdar D., 2002. "Conditional Second-Order Generalized Estimating Equations for Generalized Linear and Nonlinear Mixed-Effects Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 271-283, March.
    4. Shaun R. Seaman & Ian R. White & Andrew J. Copas & Leah Li, 2012. "Combining Multiple Imputation and Inverse-Probability Weighting," Biometrics, The International Biometric Society, vol. 68(1), pages 129-137, March.
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