Integrating randomized and observational studies to estimate optimal dynamic treatment regimes.
Biometrics
; 80(2)2024 Mar 27.
Article
em En
| MEDLINE
| ID: mdl-38804219
ABSTRACT
Sequential multiple assignment randomized trials (SMARTs) are the gold standard for estimating optimal dynamic treatment regimes (DTRs), but are costly and require a large sample size. We introduce the multi-stage augmented Q-learning estimator (MAQE) to improve efficiency of estimation of optimal DTRs by augmenting SMART data with observational data. Our motivating example comes from the Back Pain Consortium, where one of the overarching aims is to learn how to tailor treatments for chronic low back pain to individual patient phenotypes, knowledge which is lacking clinically. The Consortium-wide collaborative SMART and observational studies within the Consortium collect data on the same participant phenotypes, treatments, and outcomes at multiple time points, which can easily be integrated. Previously published single-stage augmentation methods for integration of trial and observational study (OS) data were adapted to estimate optimal DTRs from SMARTs using Q-learning. Simulation studies show the MAQE, which integrates phenotype, treatment, and outcome information from multiple studies over multiple time points, more accurately estimates the optimal DTR, and has a higher average value than a comparable Q-learning estimator without augmentation. We demonstrate this improvement is robust to a wide range of trial and OS sample sizes, addition of noise variables, and effect sizes.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Simulação por Computador
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Ensaios Clínicos Controlados Aleatórios como Assunto
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Dor Lombar
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Estudos Observacionais como Assunto
Limite:
Humans
Idioma:
En
Revista:
Biometrics
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos