Implementing statistical methods for generalizing randomized trial findings to a target population.
Addict Behav
; 94: 124-132, 2019 07.
Article
en En
| MEDLINE
| ID: mdl-30415786
ABSTRACT
Randomized trials are considered the gold standard for assessing the causal effects of a drug or intervention in a study population, and their results are often utilized in the formulation of health policy. However, there is growing concern that results from trials do not necessarily generalize well to their respective target populations, in which policies are enacted, due to substantial demographic differences between study and target populations. In trials related to substance use disorders (SUDs), especially, strict exclusion criteria make it challenging to obtain study samples that are fully "representative" of the populations that policymakers may wish to generalize their results to. In this paper, we provide an overview of post-trial statistical methods for assessing and improving upon the generalizability of a randomized trial to a well-defined target population. We then illustrate the different methods using a randomized trial related to methamphetamine dependence and a target population of substance abuse treatment seekers, and provide software to implement the methods in R using the "generalize" package. We discuss several practical considerations for researchers who wish to utilize these tools, such as the importance of acquiring population-level data to represent the target population of interest, and the challenges of data harmonization.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
2_ODS3
Problema de salud:
2_sustancias_psicoativas
Asunto principal:
Proyectos de Investigación
/
Ensayos Clínicos Controlados Aleatorios como Asunto
/
Modelos Estadísticos
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Trastornos Relacionados con Anfetaminas
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Necesidades y Demandas de Servicios de Salud
Tipo de estudio:
Clinical_trials
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Diagnostic_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Addict Behav
Año:
2019
Tipo del documento:
Article