TY - JOUR

T1 - Robust inference using hierarchical likelihood approach for heavy-tailed longitudinal outcomes with missing data

T2 - An alternative to inverse probability weighted generalized estimating equations

AU - Lee, Donghwan

AU - Lee, Youngjo

AU - Paik, Myunghee Cho

AU - Kenward, Michael G.

N1 - Funding Information:
This work was supported by the National Research Foundation (NRF) of Korea grant funded by the Korean government (MEST) (No. 2011-0030810 ).

PY - 2013/3

Y1 - 2013/3

N2 - 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.

AB - 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.

KW - Generalized estimating equations

KW - Hierarchical likelihood

KW - Inverse probability weighting

KW - Missing at random

UR - http://www.scopus.com/inward/record.url?scp=84870062564&partnerID=8YFLogxK

U2 - 10.1016/j.csda.2012.10.013

DO - 10.1016/j.csda.2012.10.013

M3 - Article

AN - SCOPUS:84870062564

SN - 0167-9473

VL - 59

SP - 171

EP - 179

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

IS - 1

ER -