## 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