Abstract:
Statistical modeling of multilevel data has been in discussion for several years and
many developments have been made in this aspect. However the field of multilevel
modeling for discrete categorical responses is relatively new, with markedly few
applications in the areas of ordinal categorical response modeling. Most of these
applications are focused in the area of educational data. The basis of this paper is
to explore the use of Generalized Linear Multilevel Models for modeling a
multilevel ordinal categorical response, in the field of medicine, which is somewhat
of a novel application, as these methods have seldom been utilized in modeling
medical data. The application focuses on analysing the factors that affect the
severity of respiratory infections diagnosed in family practice and is based on data
collected at 13 family practices in Sri Lanka. The data consisted of individual
patient records, clustered within the practices and thus required a multilevel
modeling approach. The explanatory variables pertaining to this study were: Age,
Gender, Duration and most prevailing Symptom of the patients, while the ordinal
categorical response indicating the severity of the diagnosis made was termed
Diagnosis. Two main approaches of the Generalized Linear Multilevel Model;
namely the Proportional Odds Model and the Non-Proportional Odds Model have
been applied to the data and the models compared using suitable diagnostic tests.
The variables Symptom and Duration provided significant main effects while the
Symptom-Gender interaction also proved to be significant. Based on the DIC
diagnostic, the Non-Proportional Odds model proves to be the better of the two
models.
