Models for cluster randomized designs using ranked set sampling.
Stat Med
; 42(15): 2692-2710, 2023 07 10.
Article
en En
| MEDLINE
| ID: mdl-37041108
Cluster randomized designs (CRD) provide a rigorous development for randomization principles for studies where treatments are allocated to cluster units rather than the individual subjects within clusters. It is known that CRDs are less efficient than completely randomized designs since the randomization of treatment allocation is applied to the cluster units. To mitigate this problem, we embed a ranked set sampling design from survey sampling studies into CRD for the selection of both cluster and subsampling units. We show that ranking groups in ranked set sampling act like a covariate, reduce the expected mean squared cluster error, and increase the precision of the sampling design. We provide an optimality result to determine the sample sizes at cluster and sub-sample level. We apply the proposed sampling design to a dental study on human tooth size, and to a longitudinal study from an education intervention program.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Proyectos de Investigación
Tipo de estudio:
Clinical_trials
/
Observational_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Stat Med
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos