Morphometric panel regression equations for predicting body mass in immature humans.

Journal Article (Journal Article)

OBJECTIVES: Predicting body mass is a frequent objective of several anthropological subdisciplines, but there are few published methods for predicting body mass in immature humans. Because most reference samples are composed of adults, predicting body mass outside the range of adults requires extrapolation, which may reduce the accuracy of predictions. Prediction equations developed from a sample of immature humans would reduce extrapolation for application to small-bodied target individuals, and should have utility in multiple predictive contexts. MATERIALS AND METHODS: Here, we present two novel body mass prediction equations derived from 3468 observations of stature and bi-iliac breadth from a large sample of immature humans (n = 173) collected in the Harpenden Growth Study. Prediction equations were generated using raw and natural log-transformed data and modeled using panel regression, which accounts for serial autocorrelation of longitudinal observations. Predictive accuracy was gauged with a global sample of human juveniles (n = 530 age- and sex-specific annual means) and compared to the performance of the adult morphometric prediction equation previously identified as most accurate for human juveniles. RESULTS: While the raw data panel equation is only slightly more accurate than the adult equation, the logged data panel equation generates very accurate body mass predictions across both sexes and all age classes of the test sample (mean absolute percentage prediction error = 2.47). DISCUSSION: The logged data panel equation should prove useful in archaeological, forensic, and paleontological contexts when predictor variables can be measured with confidence and are outside the range of modern adult humans.

Full Text

Duke Authors

Cited Authors

  • Yapuncich, GS; Churchill, SE; Cameron, N; Walker, CS

Published Date

  • May 2018

Published In

Volume / Issue

  • 166 / 1

Start / End Page

  • 179 - 195

PubMed ID

  • 29369332

Electronic International Standard Serial Number (EISSN)

  • 1096-8644

Digital Object Identifier (DOI)

  • 10.1002/ajpa.23422


  • eng

Conference Location

  • United States