M-estimation for common epidemiological measures: introduction and applied examples.
Int J Epidemiol
; 53(2)2024 Feb 14.
Article
en En
| MEDLINE
| ID: mdl-38423105
ABSTRACT
M-estimation is a statistical procedure that is particularly advantageous for some comon epidemiological analyses, including approaches to estimate an adjusted marginal risk contrast (i.e. inverse probability weighting and g-computation) and data fusion. In such settings, maximum likelihood variance estimates are not consistent. Thus, epidemiologists often resort to bootstrap to estimate the variance. In contrast, M-estimation allows for consistent variance estimates in these settings without requiring the computational complexity of the bootstrap. In this paper, we introduce M-estimation and provide four illustrative examples of implementation along with software code in multiple languages. M-estimation is a flexible and computationally efficient estimation procedure that is a powerful addition to the epidemiologist's toolbox.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Epidemiólogos
/
Lenguaje
Límite:
Humans
Idioma:
En
Revista:
Int J Epidemiol
/
Int. j. epidemiol
/
International journal of epidemiology
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Reino Unido