Accountable survival contrast-learning for optimal dynamic treatment regimes.

ABSTRACT: Dynamic treatment regime (DTR) is an emerging paradigm in recent medical studies, which searches a series of decision rules to assign optimal treatments to each patient by taking into account individual features such as genetic, environmental, and social factors. Although there is a large and growing literature on statistical methods to estimate optimal treatment regimes, most methodologies focused on complete data. In this article, we propose an accountable contrast-learning algorithm for optimal dynamic treatment regime with survival endpoints. Our estimating procedure is originated from a doubly-robust weighted classification scheme, which is a model-based contrast-learning method that directly characterizes the interaction terms between predictors and treatments without main effects. T

SUBMITTER: Choi T 

PROVIDER: S-EPMC9908913 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature