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 language | English |
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Pages (from-to) | 171-179 |
Number of pages | 9 |
Journal | Computational Statistics and Data Analysis |
Volume | 59 |
Issue number | 1 |
DOIs | |
State | Published - 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