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Personalized treatment selection via the covariatespecific treatment effect curve for longitudinal data

Yanghui Liu ,

School of Statistics, East China Normal University, Shanghai, People’s Republic of China

Riquan Zhang ,

School of Statistics, East China Normal University, Shanghai, People’s Republic of China

Shujie Ma ,

Department of Statistics, University of California, Riverside, CA, USA

Xiuzhen Zhang

School of Statistics, East China Normal University, Shanghai, People’s Republic of China

Pages 253-264 | Received 13 Nov. 2019, Accepted 25 Apr. 2020, Published online: 14 May. 2020,
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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