Subadult Age Estimation Using the Mixed Cumulative Probit and a Contemporary United States Population

Abstract

The mixed cumulative probit (MCP), a new, flexible algorithm that accommodates a variety of mean and shape parameters in univariate models and conditional dependence/independence in multivariate models, was used to develop subadult age estimation models. Sixty-two variables were collected on computed tomography (CT) images of 1317 individuals (537 females and 780 males) aged between birth and 21 years from the United States sample in the Subadult Virtual Anthropology Database (SVAD). Long bone measurements (n = 18), stages of epiphyseal fusion and ossification (n = 28), and stages of dental development of permanent teeth (n = 16) were used in univariate, multivariate, and mixed models and compared using test mean log posterior (TMNLP), root mean squared error (RMSE), and percent accuracy on an independent test sample. Out of the six possible parameter combinations, all combinations were accounted for at least once in the data and conditionally dependent models outperformed the conditionally independent models. Overall, multivariate models exhibited smaller TMNLP and RMSE, and an overall greater stability in the age estimations compared to univariate models across all ages and independent of indicator type. Pre-optimized subadult age estimation models are freely available for immediate application through MCP-S-Age, a graphical user interface.