Estimating average treatment effects for clustered RCTs with recruitment bias.
Stat Med
; 43(3): 452-474, 2024 02 10.
Article
en 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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Sesgo
/
Ensayos Clínicos Controlados Aleatorios como Asunto
Límite:
Humans
Idioma:
En
Revista:
Stat Med
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos