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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

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  • Lee, Donghwan
  • Lee, Youngjo
  • Paik, Myunghee Cho
  • Kenward, Michael G.

Abstract

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.

Suggested Citation

  • Lee, Donghwan & Lee, Youngjo & Paik, Myunghee Cho & Kenward, Michael G., 2013. "Robust inference using hierarchical likelihood approach for heavy-tailed longitudinal outcomes with missing data: An alternative to inverse probability weighted generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 171-179.
    • Handle: RePEc:eee:csdana:v:59:y:2013:i:c:p:171-179
    DOI: 10.1016/j.csda.2012.10.013
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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.
    2. 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.
    3. 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, November.
    4. 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, June.
    5. Zhiqiang Tan, 2010. "Bounded, efficient and doubly robust estimation with inverse weighting," Biometrika, Biometrika Trust, vol. 97(3), pages 661-682.
    6. 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.
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    Cited by:

    1. G. Inan & R. Yucel, 2017. "Joint GEEs for multivariate correlated data with incomplete binary outcomes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 1920-1937, August.

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