Estimating optimal individualized treatment rules with multistate processes.

ABSTRACT: Multistate process data are common in studies of chronic diseases such as cancer. These data are ideal for precision medicine purposes as they can be leveraged to improve more refined health outcomes, compared to standard survival outcomes, as well as incorporate patient preferences regarding quantity versus quality of life. However, there are currently no methods for the estimation of optimal individualized treatment rules with such data. In this paper, we propose a nonparametric outcome weighted learning approach for this problem in randomized clinical trial settings. The theoretical properties of the proposed methods, including Fisher consistency and asymptotic normality of the estimated expected outcome under the estimated optimal individualized treatment rule, are rigorously establish

SUBMITTER: Bakoyannis G 

PROVIDER: S-EPMC10553793 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature