TY - JOUR
T1 - Haplotype-based pharmacogenetic analysis for longitudinal quantitative traits in the presence of dropout
AU - Tzeng, Jung Ying
AU - Lu, Wenbin
AU - Farmen, Mark W.
AU - Liu, Youfang
AU - Sullivan, Patrick F.
N1 - Funding Information:
J.Y.T. was supported by NSF grant DMS-0504726 and NIH grants R01 MH074027 and R01 MH084022. W.L. was supported by NSF grant DMS-0504269 and NIH grant R01 CA140632. P.S.F. was supported by NIH grants R01 MH074027, R01 MH080403, and R01 MH084022. The CATIE project was funded by NIMH contract N01 MH90001 (PIs Drs. Jeffrey Lieberman and Scott Stroup). Jung-Ying Tzeng and Wenbin Lu are contributed equally to this work.
PY - 2010/3
Y1 - 2010/3
N2 - We propose a variety of methods based on the generalized estimation equations to address the issues encountered in haplotype-based pharmacogenetic analysis, including analysis of longitudinal data with outcome-dependent dropouts, and evaluation of the high-dimensional haplotype and haplotype-drug interaction effects in an overall manner. We use the inverse probability weights to handle the outcome-dependent dropouts under the missing-at-random assumption, and incorporate the weighted L1 penalty to select important main and interaction effects with high dimensionality. The proposed methods are easy to implement, computationally efficient, and provide an optimal balance between false positives and false negatives in detecting genetic effects.
AB - We propose a variety of methods based on the generalized estimation equations to address the issues encountered in haplotype-based pharmacogenetic analysis, including analysis of longitudinal data with outcome-dependent dropouts, and evaluation of the high-dimensional haplotype and haplotype-drug interaction effects in an overall manner. We use the inverse probability weights to handle the outcome-dependent dropouts under the missing-at-random assumption, and incorporate the weighted L1 penalty to select important main and interaction effects with high dimensionality. The proposed methods are easy to implement, computationally efficient, and provide an optimal balance between false positives and false negatives in detecting genetic effects.
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U2 - 10.1080/10543400903572787
DO - 10.1080/10543400903572787
M3 - Article
C2 - 20309762
AN - SCOPUS:77951292588
SN - 1054-3406
VL - 20
SP - 334
EP - 350
JO - Journal of Biopharmaceutical Statistics
JF - Journal of Biopharmaceutical Statistics
IS - 2
ER -