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

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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Pages (from-to)171-179
Number of pages9
JournalComputational Statistics and Data Analysis
Volume59
Issue number1
DOIs
StatePublished - Mar 2013

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation (NRF) of Korea grant funded by the Korean government (MEST) (No. 2011-0030810 ).

Keywords

  • Generalized estimating equations
  • Hierarchical likelihood
  • Inverse probability weighting
  • Missing at random

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