Masked analysis for small-scale cluster randomized controlled trials.
Behav Res Methods
; 54(4): 1701-1714, 2022 08.
Article
en En
| MEDLINE
| ID: mdl-34608614
ABSTRACT
Researchers conducting small-scale cluster randomized controlled trials (RCTs) during the pilot testing of an intervention often look for evidence of promise to justify an efficacy trial. We developed a method to test for intervention effects that is adaptive (i.e., responsive to data exploration), requires few assumptions, and is statistically valid (i.e., controls the type I error rate), by adapting masked visual analysis techniques to cluster RCTs. We illustrate the creation of masked graphs and their analysis using data from a pilot study in which 15 high school programs were randomly assigned to either business as usual or an intervention developed to promote psychological and academic well-being in 9th grade students in accelerated coursework. We conclude that in small-scale cluster RCTs there can be benefits of testing for effects without a priori specification of a statistical model or test statistic.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Proyectos de Investigación
/
Modelos Estadísticos
Tipo de estudio:
Clinical_trials
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Behav Res Methods
Asunto de la revista:
CIENCIAS DO COMPORTAMENTO
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos