Integrating decision modelling and machine learning to inform treatment stratification

Abstract:

There is increasing interest in moving away from “one size fits all' approaches towards stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates (treatment effect heterogeneity) is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn treatment effect heterogeneity without pre-specifying subgroups or functional forms, enabling the construction of decision rules (“optimal policies”) that map individual covariates into a treatment decision. However, these methods do not yet integrate ML estimates into a decision modelling framework in order to reflect long-term policy-relevant outcomes and synthesise information from multiple sources. In this paper, we propose a method to integrate ML and decision modelling, when individual patient data is available to estimate treatment-specific survival time. We also propose a novel implementation of optimal policy algorithms to define subgroups using decision model output.