TY - JOUR
T1 - Precision medicine
T2 - Subgroup identification in longitudinal trajectories
AU - Wei, Yishu
AU - Liu, Lei
AU - Su, Xiaogang
AU - Zhao, Lihui
AU - Jiang, Hongmei
N1 - Funding Information:
We want to thank Marquis (Jue) Hou from the University of California, San Diego, for helpful suggestions, and Mindy Hong from Northwestern University for editorial help. The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH.
Publisher Copyright:
© The Author(s) 2020.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - In clinical studies, the treatment effect may be heterogeneous among patients. It is of interest to identify subpopulations which benefit most from the treatment, regardless of the treatment’s overall performance. In this study, we are interested in subgroup identification in longitudinal studies when nonlinear trajectory patterns are present. Under such a situation, evaluation of the treatment effect entails comparing longitudinal trajectories while subgroup identification requires a further evaluation of differential treatment effects among subgroups induced by moderators. To this end, we propose a tree-structured subgroup identification method, termed “interaction tree for longitudinal trajectories”, which combines mixed effects models with regression splines to model the nonlinear progression patterns among repeated measures. Extensive simulation studies are conducted to evaluate its performance and an application to an alcohol addiction pharmacogenetic trial is presented.
AB - In clinical studies, the treatment effect may be heterogeneous among patients. It is of interest to identify subpopulations which benefit most from the treatment, regardless of the treatment’s overall performance. In this study, we are interested in subgroup identification in longitudinal studies when nonlinear trajectory patterns are present. Under such a situation, evaluation of the treatment effect entails comparing longitudinal trajectories while subgroup identification requires a further evaluation of differential treatment effects among subgroups induced by moderators. To this end, we propose a tree-structured subgroup identification method, termed “interaction tree for longitudinal trajectories”, which combines mixed effects models with regression splines to model the nonlinear progression patterns among repeated measures. Extensive simulation studies are conducted to evaluate its performance and an application to an alcohol addiction pharmacogenetic trial is presented.
KW - Recursive partitioning
KW - interaction tree
KW - personalized medicine
KW - precision medicine
UR - http://www.scopus.com/inward/record.url?scp=85081648517&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081648517&partnerID=8YFLogxK
U2 - 10.1177/0962280220904114
DO - 10.1177/0962280220904114
M3 - Article
C2 - 32070237
AN - SCOPUS:85081648517
SN - 0962-2802
VL - 29
SP - 2603
EP - 2616
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 9
ER -