Step-adjusted tree-based reinforcement learning for evaluating nested dynamic treatment regimes using test-and-treat observational data.

ABSTRACT: Dynamic treatment regimes (DTRs) include a sequence of treatment decision rules, in which treatment is adapted over time in response to the changes in an individual's disease progression and health care history. In medical practice, nested test-and-treat strategies are common to improve cost-effectiveness. For example, for patients at risk of prostate cancer, only patients who have high prostate-specific antigen (PSA) need a biopsy, which is costly and invasive, to confirm the diagnosis and help determine the treatment if needed. A decision about treatment happens after the biopsy, and is thus nested within the decision of whether to do the test. However, current existing statistical methods are not able to accommodate such a naturally embedded property of the treatment decision within the

SUBMITTER: Tang M 

PROVIDER: S-EPMC8595655 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature