Comparison of Statistical Models for the Estimation of Age at Death Using Subjective Adult Human Dental Indicators

Abstract

Objective: The main objective of this paper is the estimation of age at death using subjective dental data. This is particularly useful in developing and under developed countries. Methods: This study provides a framework for the estimation of age at death using very subjective measurements of the teeth using (i) Generalized Linear Models (GLMs) and (ii) Generalized Additive Models (GAMs). These predictors of age were all ordinal in nature. A dataset comprising measurements taken on 71 maxillary incisors from different individuals at the time of their death was used. A comparison of two models – the Gamma GLM and the Gamma GAM is used to illustrate the flexibility of this method and the predictive power of the statistical modelling process. Results: The study showed the effectiveness of the models through the Akaike Information Criterion (AIC) as well as the proportion of correct predictions within each of the age groups. The Gamma GAM actually had the higher AIC but the better predictive values within the age groups. Conclusion: Statistical modelling caters for the types of data and can give reasonable predictions of age at death.