Doctoral Dissertation Research: The conditional independence assumption in juvenile dental age estimation

Estimating the age of individuals based on skeletal elements can inform our understanding of human biocultural experiences, identities, and population dynamics across the history of our species. Methods that use information about dental development are particularly useful for juvenile age estimation. This doctoral dissertation research project examines the developmental relationships between teeth and how these relationships affect error rates in juvenile dental age estimation.

A better understanding of human variation in dental correlations can improve both methods of age estimation for bioarchaeological and forensic research as well as clinical understanding of human growth and development. The project supports graduate training in STEM, the development of open-source dental age estimation software, and online training workshops for researchers and practitioners.

In order to simplify mathematical modeling, it is often assumed that dental developmental traits are independent from one another after accounting for the effect of chronological age. The problem with this conditional independence assumption is that if conditional independence is not biologically valid, resulting age estimates will be biased and have error rates that are larger than expected.

This is a problem in both bioarchaeology and forensic anthropology because biased age estimates can compromise our understanding of past populations and unknown age interval error rates do not meet the Daubert standard for expert testimony. These issues are addressed through three specific aims: 1) characterize the extent of correlations between developing teeth, 2) characterize the degree of variability of correlations between developing teeth, and 3) develop, validate, and test an open-source developmental age estimation program that incorporates the correlations between teeth.

These aims are approached using Bayesian statistical models and cluster analysis in R. Models are fit to dental development scores of living children and validated using dental development scores from existing decedent databases in order to develop the age estimation software.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.