Tailored logistic regression models for sex estimation of unknown individuals using the published population data of the humeral epiphyses

This paper introduces a far-flung approach to formulate population independent models based on the humeral epiphyses as a supplementary tool for biological sex estimation of unknown partial remains. Resources for this study include the published summary statistics of 7 modern populations inhabited the continents of Africa, Asia, Europe, and South America. The regenerated humeral metric data (n = 1490) via truncation approach were modeled using logistic regression. Three fitted models were evaluated for applicability across populations on an independent test sample (n = 430). The experiment was assessed graphically and quantitatively using histogram of posterior probabilities and the classification table. The predictive power of the models was evaluated at the conventional (0.5) and high (0.95) posterior probability thresholds. It was found that the vertical humeral head model is insufficient for sex estimation especially in the European females due to different levels of inter-population size variability. Interestingly, the distal biepicondylar breadth model showed overall better performance achieving the highest total and sex specific accuracies. Findings indicated that together, the epiphyseal measurements are capable of discriminating sex with overall accuracy of 90.2% which is raised up to 98.8% with 95% confidence of accurate estimates in more than 50% of the test sample. While evidences have been presented pointing to the biological and statistical meaningfulness of the humeral epiphyses model, the analysis allowed pinpointing the utility of the distal biepicondylar breadth model in sex diagnosis in transpopulation application settings. Additionally, few variables are needed to reach satisfactory sex prediction in a diverse sample.