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 -