Estimating average treatment effects for clustered RCTs with recruitment bias.
Stat Med
; 43(3): 452-474, 2024 02 10.
Article
em En
| MEDLINE
| ID: mdl-38037270
ABSTRACT
In clustered randomized controlled trials (RCTs), sample recruitment is often conducted after cluster randomization. This timing can lead to recruitment bias if access to the intervention affects the composition of study-eligible cluster entrants and study consenters. This article develops a potential outcomes framework in such settings that yields a causal estimand that pertains to the always-recruited in either research condition. A consistent inverse probability weighting (IPW) estimator is developed using data on recruits only, and a generalized estimating equations approach is used to obtain robust clustered SE estimators that adjust for estimation error in the IPW weights. A simple data collection strategy is discussed to improve the predictive accuracy of the logit propensity score models. Simulations show that the IPW estimator achieves nominal confidence interval coverage under the assumed identification conditions. An empirical application demonstrates the methods using data from an RCT testing the effects of a behavioral health intervention in schools. An R program for estimation is available for download.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Viés
/
Ensaios Clínicos Controlados Aleatórios como Assunto
Limite:
Humans
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos