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

Yanghui Liu, Riquan Zhang, Shujie Ma and Xiuzhen Zhang

Statistical Theory and Related Fields, 2021, vol. 5, issue 3, 253-264

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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/24754269.2020.1762059 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tstfxx:v:5:y:2021:i:3:p:253-264

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tstf20

DOI: 10.1080/24754269.2020.1762059

Access Statistics for this article

Statistical Theory and Related Fields is currently edited by Zhao Wei

More articles in Statistical Theory and Related Fields from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().