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Robust inference using hierarchical likelihood approach for heavy-tailed longitudinal outcomes with missing data: An alternative to inverse probability weighted generalized estimating equations

  • Lee, Donghwan
  • Lee, Youngjo
  • Paik, Myunghee Cho
  • Kenward, Michael G.
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    We examine methods appropriate for heavy-tailed longitudinal outcomes with possibly missing data. Generalized estimating equations (GEEs) have been widely used in longitudinal studies when data are not heavy-tailed and, in general, are valid only when data are missing completely at random. Robins et al. (1995) showed how inverse probability weighting in such settings (IPW-GEE) can extend validity to data that are missing at random. When data are completely observed, Preisser and Qaqish (1999) proposed the use of robust GEE methods to handle outliers. A natural extension of this to the setting with missing data is to combine these two methods. One alternative for the same setting is to use hierarchical (h-) likelihood (Lee et al., 2006). Here we compare this approach with that of IPW-GEE for heavy-tailed data in the missing data context.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0167947312003817
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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 59 (2013)
    Issue (Month): C ()
    Pages: 171-179

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    Handle: RePEc:eee:csdana:v:59:y:2013:i:c:p:171-179
    Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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    1. Cantoni, Eva & Ronchetti, Elvezio, 2006. "A robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures," Journal of Health Economics, Elsevier, vol. 25(2), pages 198-213, March.
    2. John S. Preisser & Bahjat F. Qaqish, 1999. "Robust Regression for Clustered Data with Application to Binary Responses," Biometrics, The International Biometric Society, vol. 55(2), pages 574-579, 06.
    3. Zhiqiang Tan, 2010. "Bounded, efficient and doubly robust estimation with inverse weighting," Biometrika, Biometrika Trust, vol. 97(3), pages 661-682.
    4. Philippe Huber & Elvezio Ronchetti & Maria-Pia Victoria-Feser, 2004. "Estimation of generalized linear latent variable models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 893-908.
    5. Sung-Cheol Yun & Youngjo Lee & Michael G. Kenward, 2007. "Using Hierarchical Likelihood for Missing Data Problems," Biometrika, Biometrika Trust, vol. 94(4), pages 905-919.
    6. 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, 06.
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