Abstract
Treatment selection based on patient characteristics has been widely recognised in modern medicine. In this paper, we propose a generalised partially linear single-index mixed-effects modelling strategy for treatment selection and heterogeneous treatment effect estimation in longitudinal clinical and observational studies. We model the treatment effect as an unknown functional curve of a weighted linear combination of time-dependent covariates. This method enables us to investigate covariate-specific treatment effects and make personalised treatment selection in a flexible fashion. We develop a method that combines local linear regression and penalised quasi-likelihood to estimate the weight for each covariate, the unknown treatment effect curve and the parameters for mixed-effects. Based on pointwise confidence intervals for the treatment effect curve, we can make individualised treatment decisions from the information of patient characteristics. A simulation study is conducted to evaluate finite sample performance of the proposed method. We also illustrate the method via analysis of a real data example.
Acknowledgments
We are grateful to the Editor-in-Chief, Prof. Jun Shao, an Associate Editor and anonymous reviewers for their thorough reading of our manuscript and insightful comments that have led to significant improvement of this work. We also thank the US National Alzheimer's Coordinating Center for providing the data.
Disclosure statement
No potential conflict of interest was reported by the author(s).
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Notes on contributors
Yanghui Liu
Yanghui Liu is a Ph.D. candidate in Statistics at East China Normal University.
Riquan Zhang
Riquan Zhang is a Professor in School of Statistics at East China Normal University.
Shujie Ma
Shujie Ma is an Associate Professor in Department of Statistics at University of California, Riverside.
Xiuzhen Zhang
Xiuzhen Zhang is a Ph.D. candidate in Statistics at East China Normal University.