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

Author

Listed:
  • Yanghui Liu
  • Riquan Zhang
  • Shujie Ma
  • Xiuzhen Zhang

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.

Suggested Citation

  • 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, Taylor & Francis Journals, vol. 5(3), pages 253-264, July.
    • Handle: RePEc:taf:tstfxx:v:5:y:2021:i:3:p:253-264
    DOI: 10.1080/24754269.2020.1762059
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