School of Statistics, East China Normal University, Shanghai, People’s Republic of China
School of Statistics, East China Normal University, Shanghai, People’s Republic of China
Department of Statistics, University of California, Riverside, CA, USA
School of Statistics, East China Normal University, Shanghai, People’s Republic of China
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.
To cite this article: Yanghui Liu, Riquan Zhang, Shujie Ma & Xiuzhen Zhang (2021) Personalized treatment selection via the covariate-specific treatment effect curve for longitudinal data, Statistical Theory and Related Fields, 5:3, 253-264, DOI: 10.1080/24754269.2020.1762059
To link to this article: https://doi.org/10.1080/24754269.2020.1762059