Integrating decision modeling and machine learning to inform treatment stratification.
Health Econ
; 33(8): 1772-1792, 2024 Aug.
Article
em En
| MEDLINE
| ID: mdl-38664948
ABSTRACT
There is increasing interest in moving away from "one size fits all (OSFA)" approaches toward 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 modeling framework in order to reflect long-term policy-relevant outcomes and synthesize information from multiple sources. In this paper, we propose a method to integrate ML and decision modeling, 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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Técnicas de Apoio para a Decisão
/
Análise Custo-Benefício
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Aprendizado de Máquina
Limite:
Female
/
Humans
/
Male
Idioma:
En
Revista:
Health Econ
Assunto da revista:
SERVICOS DE SAUDE
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Reino Unido