Self-matched learning to construct treatment decision rules from electronic health records.
Stat Med
; 41(17): 3434-3447, 2022 07 30.
Article
en En
| MEDLINE
| ID: mdl-35511090
ABSTRACT
Electronic health records (EHRs) collected from large-scale health systems provide rich subject-specific information on a broad patient population at a lower cost compared to randomized controlled trials. Thus, EHRs may serve as a complementary resource to provide real-world data to construct individualized treatment rules (ITRs) and achieve precision medicine. However, in the absence of randomization, inferring treatment rules from EHR data may suffer from unmeasured confounding. In this article, we propose a self-matched learning method inspired by the self-controlled case series (SCCS) design to mitigate this challenge. We alleviate unmeasured time-invariant confounding between patients by matching different periods of treatments within the same patient (self-controlled matching) to infer the optimal ITRs. The proposed method constructs a within-subject matched value function for optimizing ITRs and bears similarity to the SCCS design. We examine assumptions that ensure Fisher consistency, and show that our method requires weaker assumptions on unmeasured confounding than alternative methods. Through extensive simulation studies, we demonstrate that self-matched learning has comparable performance to other existing methods when there are no unmeasured confounders, but performs markedly better when unobserved time-invariant confounders are present, which is often the case for EHRs. Sensitivity analyses show that the proposed method is robust under different scenarios. Finally, we apply self-matched learning to estimate the optimal ITRs from type 2 diabetes patient EHRs, which shows our estimated decision rules lead to greater advantages in reducing patients' diabetes-related complications.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Diabetes Mellitus Tipo 2
/
Registros Electrónicos de Salud
Tipo de estudio:
Clinical_trials
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Stat Med
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos