Subadult age estimation variables: Exploring their varying roles across ontogeny

Stull, Kyra E.; Corron, Louise; Price, Michael Holton

doi:10.1016/b978-0-12-824370-1.00003-1

Cited by 3 publications

(7 citation statements)

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“…That is, skeletal age intervals are typically no longer than a few years, and often less. Nevertheless, the logical and analytical issues discussed for adults also pertain to subadults (Kamnikar et al, 2018; Kumagai et al, 2018; Stull et al, 2021).…”

Section: Age Estimationmentioning

confidence: 99%

Paleodemography: From archaeology and skeletal age estimation to life in the past

Boldsen

Milner

Ousley

2021

American Journal of Biological Anthropology

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Much of paleodemography, an interdisciplinary field with strong ties to archaeology, among other disciplines, is oriented toward clarifying the life experiences of past people and why they changed over time. We focus on how human skeletons contribute to our understanding of preindustrial demographic regimes, including when changes took place that led to the world as we know it today. Problems with existing paleodemographic practices are highlighted, as are promising directions for future work. The latter requires both better age estimates and innovative methods to handle data appropriately. Age‐at‐death estimates for adult skeletons are a particular problem, especially for adults over 50 years that undoubtedly are mistakenly underrepresented in published studies of archaeological skeletons. Better age estimates for the entirety of the lifespan are essential to generate realistic distributions of age at death. There are currently encouraging signs that after about a half‐century of intensive, and sometimes contentious, research, paleodemography is poised to contribute much to understandings of evolutionary processes, the structure of past populations, and human‐disease interaction, among other topics.

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Section: Age Estimationmentioning

confidence: 99%

Paleodemography: From archaeology and skeletal age estimation to life in the past

Boldsen

Milner

Ousley

2021

American Journal of Biological Anthropology

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“…That is, while we allow for the possibility that the mean response and diagonal terms of the multivariate covariate matrix are age dependent, we do not allow for age dependence in the correlations, which contradicts preliminary findings we report in Stull et al . [60]. In assessing the presence of conditional correlations, we only tested a four-group model (conditionally dependent model) for comparison against a conditionally independent model.…”

Section: Discussionmentioning

confidence: 99%

“…The two diaphyseal lengths were taken to the nearest hundredth of a millimetre on three-dimensional surface reconstructions of the corresponding skeletal elements following definitions for virtual elements. The four ordinal variables were scored directly on CT scans of the individuals using three different scales [59]. Epiphyseal fusion for the humeral medial epicondyle (HME_EF) was collected using a seven-stage system but collapsed into a four-stage system of absent (0), present (1), active fusion (1/2, 2, 2/3, 3) and fused (4) [60].…”

Section: Methodsmentioning

confidence: 99%

“…The four ordinal variables were scored directly on CT scans of the individuals using three different scales [59]. Epiphyseal fusion for the humeral medial epicondyle (HME_EF) was collected using a seven-stage system but collapsed into a four-stage system of absent (0), present (1), active fusion (1/2, 2, 2/3, 3) and fused (4) [60]. The number of tarsal bones (TC_Oss) present ranged from 0 (none present) to 7 (all present) though similar appearance times led to collapsing into six stages (calcaneus and talus were collapsed).…”

Section: Methodsmentioning

confidence: 99%

“…All data were collected through the Amira™ three-dimensional visualization and reconstruction software (Amira™ v. 6.7.0. 1999–2018 FEI SAS, a part of Thermo Fisher Scientific), and detailed methodologies for all variables and their associated error rates can be found in the SVAD data collection protocols [59,60] and in Corron et al . 's study [62].…”

Section: Methodsmentioning

confidence: 99%

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Mixed cumulative probit: a multivariate generalization of transition analysis that accommodates variation in the shape, spread and structure of data

et al. 2023

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Biological data are frequently nonlinear, heteroscedastic and conditionally dependent, and often researchers deal with missing data. To account for characteristics common in biological data in one algorithm, we developed the mixed cumulative probit (MCP), a novel latent trait model that is a formal generalization of the cumulative probit model usually used in transition analysis. Specifically, the MCP accommodates heteroscedasticity, mixtures of ordinal and continuous variables, missing values, conditional dependence and alternative specifications of the mean response and noise response. Cross-validation selects the best model parameters (mean response and the noise response for simple models, as well as conditional dependence for multivariate models), and the Kullback–Leibler divergence evaluates information gain during posterior inference to quantify mis-specified models (conditionally dependent versus conditionally independent). Two continuous and four ordinal skeletal and dental variables collected from 1296 individuals (aged birth to 22 years) from the Subadult Virtual Anthropology Database are used to introduce and demonstrate the algorithm. In addition to describing the features of the MCP, we provide material to help fit novel datasets using the MCP. The flexible, general formulation with model selection provides a process to robustly identify the modelling assumptions that are best suited for the data at hand.

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Addressing the growing fossil record of subadult hominins by reaching across disciplines

Bolter,

Cameron,

Hawks

et al. 2023

Evolutionary Anthropology

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Subadult age estimation variables: Exploring their varying roles across ontogeny

Cited by 3 publications

References 20 publications

Paleodemography: From archaeology and skeletal age estimation to life in the past

Paleodemography: From archaeology and skeletal age estimation to life in the past

Mixed cumulative probit: a multivariate generalization of transition analysis that accommodates variation in the shape, spread and structure of data

Addressing the growing fossil record of subadult hominins by reaching across disciplines

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