Statistical Theory and Related Fields (Jul 2021)

Personalized treatment selection via the covariate-specific treatment effect curve for longitudinal data

  • Yanghui Liu,
  • Riquan Zhang,
  • Shujie Ma,
  • Xiuzhen Zhang

DOI
https://doi.org/10.1080/24754269.2020.1762059
Journal volume & issue
Vol. 5, no. 3
pp. 253 – 264

Abstract

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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.

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