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Vol. 13. Núm. 5.
Páginas 391-398 (Septiembre - Octubre 1999)
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Vol. 13. Núm. 5.
Páginas 391-398 (Septiembre - Octubre 1999)
Open Access
Los modelos multinivel o la importancia de la jerarquía
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E. Sánchez-Cantalejo
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esc@easp.es

Emilio Sánchez-Cantalejo Ramírez. Escuela Andaluza de Salud Pública. Campus Universitario de Cartuja, Apdo. de Correos 2070. 18080 Granada.
, R. Ocaña-Riola
Escuela Andaluza de Salud Pública (Granada)
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Resumen

Una parte importante de la investigación sanitaria proporciona bases de datos en las que se puede establecer alguna estructura jerárquica. Así, los individuos estudiados, unidades muestrales de nivel 1, pueden pertenecer a grupos o unidades mayores, por ejemplo, la zona geográfica donde viven, el médico que los atiende, el hospital que los asiste, etc., las unidades de nivel 2. La posible mayor homegeneidad entre individuos de un mismo grupo respecto a individuos de distintos grupos invalidaría la hipótesis de independencia necesaria para poder utilizar los modelos tradicionales de regresión. Los modelos multinivel, también denominados modelos jerárquicos, permiten solventar esta dificultad al distinguir los distintos niveles jerárquicos de las predictoras, separando la variabilidad de los individuos objeto de estudio de la de los grupos a los que pertenecen. Aunque estos modelos se desarrollaron en la pasada década, especialmente en el campo de la educación, en los últimos años se ha puesto en evidencia su aplicabilidad en la investigación sanitaria. Este trabajo describe algunos modelos multinivel sencillos, discute sus ventajas sobre los métodos clásicos y presenta algunos ejemplos de aplicación tanto en la investigación epidemiológica como en la de servicios sanitarios.

Palabras clave:
Análisis Multinivel
Modelos Estadísticos
Análisis de Regresión
Análisis Multivariante
Análisis Estadístico
Summary

Many researchers in Public Health have data bases with a hierarchical structure. The studied patients (level 1) can be nested in groups, i.e., district, doctor, hospital, etc. (level 2). It is possible that patients in the same group be similar, so traditional regression models can not be used because the hypothesis of independent observations is not satisfied. A Multilevel Analysis, using hierarchical models, can be a solution for this problem; these models take into account the distribution of the data at different levels to estimate two types of variability: one due to individuals in the study and another due to the groups in which patients are nested. These types of models were applied in education in the last decade, however they have been recently applied in Health Research. This paper is a review about multilevel analysis. A discussion about hierarchichal models versus traditional regression models is presented and some applications in Epidemiology and Health Research are showed.

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