Assessing Heterogeneity of Treatment Effects in Observational Studies.
Am J Epidemiol
; 190(6): 1088-1100, 2021 06 01.
Article
en En
| MEDLINE
| ID: mdl-33083822
ABSTRACT
Here we describe methods for assessing heterogeneity of treatment effects over prespecified subgroups in observational studies, using outcome-model-based (g-formula), inverse probability weighting, doubly robust, and matching estimators of subgroup-specific potential outcome means, conditional average treatment effects, and measures of heterogeneity of treatment effects. We compare the finite-sample performance of different estimators in simulation studies where we vary the total sample size, the relative frequency of each subgroup, the magnitude of treatment effect in each subgroup, and the distribution of baseline covariates, for both continuous and binary outcomes. We find that the estimators' bias and variance vary substantially in finite samples, even when there is no unobserved confounding and no model misspecification. As an illustration, we apply the methods to data from the Coronary Artery Surgery Study (August 1975-December 1996) to compare the effect of surgery plus medical therapy with that of medical therapy alone for chronic coronary artery disease in subgroups defined by previous myocardial infarction or left ventricular ejection fraction.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Asunto principal:
Estadística como Asunto
/
Interpretación Estadística de Datos
/
Modelos Estadísticos
/
Evaluación de Resultado en la Atención de Salud
/
Estudios Observacionales como Asunto
Tipo de estudio:
Observational_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Am J Epidemiol
Año:
2021
Tipo del documento:
Article