Individualized Treatment Rules: Generating Candidate Clinical Trials

Statistical methods have rarely been applied to learn individualized treatment rules, or rules for altering treatments over time in response to changes in individual covariates. Termed dynamic treatment regimes in the statistical literature, such individualized treatment rules are of primary importance in the practice of clinical medicine. History-Adjusted Marginal Structural Models (HA-MSM) estimate individualized treatment rules that assign, at each time point, the first action of the future static treatment plan that optimizes expected outcome given a patient\u27s covariates. However, as we discuss here, the optimality of these rules can depend on the way in which treatment was assigned in the data from which the rules were derived. In this article we discuss the conditions sufficient for treatment rules identified by HA-MSM to be statically optimal, or in other words, to select the optimal future static treatment plan at each time point, regardless of the way in which past treatment was assigned. The resulting treatment rules form appropriate candidates for evaluation using randomized controlled trials. We demonstrate that a history-adjusted individualized treatment rule is statically optimal if it depends on a set of covariates that are sufficient to control for confounding of the effect of past treatment history on outcome. Methods and results are illustrated using an example drawn from the antiretroviral treatment of patients infected with HIV. Specifically, we focus on rules for deciding when to modify the treatment of patients infected with resistant virus