[Multilevel models or the importance of ranking]

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.