Multivariate Statistical Modeling of the Factors Affecting Oral Health Disease – A Periodontal Disease

PhD Javali S. B.
Department of Biostatistics, SDM College of Dental Sciences, Sattur


Periodontal disease is the most common oral diseases that affect mankind and it occupies a prominent role in deciding the oral health status through out the world. In this study, an effort has been made to determine the most likely factors affecting periodontal disease and to select a meticulous model of the periodontal disease of study subjects. The data were collected by Community Periodontal Index for Treatment Needs (CPITN) index followed by WHO diagnostic criteria from a systematic random sample of 1760 subjects aged between 18-40 years in Dharwad, Karnataka, India. The Multiple Logistic Regression (MLR) was estimated; it is an effective approach for binary responses as compared with models for profiling influences of different factors. To explore the combined effect of each factor on dichotomous periodontal disease by MLR and compared the performances of full logistic model with that of reduced logistic model (step wise) using log likelihood estimate and Akaike Information Criterion (AIC). The AIC value of reduced model is smaller (1.2539) than that of full model (1.2577). It concluded that, the reduced logistic regression model is slightly a better fit as compared to full logistic regression model to the binary CPITN index data.

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Romanian Statistical Review 1/2011