Integrating decision modelling and machine learning to inform treatment stratification

TY - JOUR

T1 - Integrating decision modelling and machine learning to inform treatment stratification

AU - Glynn, David

AU - Giardina, John

AU - Hatamyar, Julia Farideh

AU - Pandya, Ankur

AU - Soares, Marta O

AU - Kreif, Noemi

N1 - © 2024 The Authors

PY - 2024/4/25

Y1 - 2024/4/25

N2 - There is increasing interest in moving away from “one size fits all (OSFA)' approaches towards stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre-specifying subgroups or functional forms, enabling the construction of decision rules (“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 policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for 'standard' and 'intensive' blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree-based algorithm can increase the estimates of the INHB.

AB - There is increasing interest in moving away from “one size fits all (OSFA)' approaches towards stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre-specifying subgroups or functional forms, enabling the construction of decision rules (“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 policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for 'standard' and 'intensive' blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree-based algorithm can increase the estimates of the INHB.

U2 - 10.1002/hec.4834

DO - 10.1002/hec.4834

M3 - Article

SN - 1057-9230

JO - Health Economics

JF - Health Economics

ER -