Multilevel models applied in the analysis of
repeated measure data: an application to Elderly
Care Program (ECP)

Genevile Carife BERGAMO[1]

José Eduardo CORRENTE[2]

§    ABSTRACT: It is common to find hierarchically structured data in several scientific studies, that is, the studied subjects are nested in the lowest level units belonging to the highest level units, and so on. To analyze this sort of data, it is important to take the hierarchical structure into account; otherwise, coefficients can be overestimated in the studied model. Then, in order to make data analysis according to the hierarchical structure easier, multilevel models were developed. Such models take into account all the existing variability for the data at the same level as well as for those in different levels of the hierarchy. In the case of repeated measure data, a two-level hierarchical structure can be considered, organizing the occasions at the first level for each subject at the second level. In this study, the multilevel models for several levels as well as the estimation methods and tests for the involved parameters in the model are approached. As an application, data from the Elderly Care Program (ECP), developed at outpatient clinic Dr. Plinio do Prado Coutinho in Alfenas, MG, where the Body Mass Indexes and Blood Pressure Rates of 22 elderly patients were observed during 22 months, were used.

§    KEYWORDS: Multilevel models; hierarchical models; repeated measures; blood pressure; body mass index.



[1]Universideda de Alfenas - UNIFENAS, CEP 37130-000, Alfenas, MG, Brasil.

[2]Departamento de Bioestatística, Instituto de Biociências, Universidade Estadual Paulista - UNESP, CEP 18618-000, Botucatu, SP, Brasil. E-mail: jecorren@ibb.unesp.br.