An analysis of clustured categorical data - application in dental health
by
Kalyan Das
Department of Statistics, Calcutta University, India
Coauthors: Asis Kr Chattopadhyay

Quite often in medical studies multiple discrete indicators are used to measure some characters that are defined only conceptually and are difficult to measure directly. Studies of this type exhibit categorical responses of dependent nature. Analysis of such categorical data appears to be extremely diffcult (intractable) particularly in the presence of risk (causal) factors. In the present article, our purpose is to develop a latent mixture regression model for analyzing such multivariate categorical data. Such a mixture model accommodates correlated and over-dispersed data through the incorporation of random effects. Unfortunately, a full likelihood anlysis is often hampered by the need for numerical integration. Two different procedures have been considered here. Both involve intensive computations. Numerical investigation has been carried out on the basis of a survey data covering 220 individuals from medical colleges in and around Calcutta (India). The purpose of the study is to compare tooth cleaning efficiency of brushes manufactured by different companies.

Date received: October 6, 2002