Personalized treatment selection via the covariate-specific treatment effect curve for longitudinal data
| Autor: | Yanghui Liu, Riquan Zhang, Shujie Ma, Xiuzhen Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Statistical Theory and Related Fields, Vol 5, Iss 3, Pp 253-264 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2475-4269 2475-4277 24754269 |
| DOI: | 10.1080/24754269.2020.1762059 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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