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Age estimation in forensic anthropology: methodological considerations about the validation studies of prediction models

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Abstract

There is currently no clear consensus on how to calculate, express, and interpret the error when validating methods for age estimation in forensic anthropology. For this reason, it is likely that researchers are commonly drawing erroneous or confusing conclusions about the existence of population differences or the need to design new and increasingly complex estimation methods. In recent years, many researchers have highlighted these limitations. They propose new lines of research focused on the use of rigorous statistics and new technologies for the development of methods for estimating age. Our main objective in this study is to contribute to the strengthening of these novel ideas, for which we show the existing empirical evidence about the inadequacy of some age estimation methods in calculating, expressing, and interpreting the errors obtained. With this aim, a total of 500 simulations have been performed, in which hypothetical research teams develop and validate methods for age estimation. The data employed in this study was obtained from the “Centers for Disease Control and Prevention (CDC) Growth Charts: United States” released in 2000. The charts relate age with height, weight, and head circumference of US male children. Five learning algorithms have been employed as age estimators. We have performed three experiments in which the following aspects have been analyzed: frequency with which “negative” results can be obtained in the validation studies; which are the most appropriate criteria to compare and select the age estimation methods; and what analysis should be employed to carry out the validation studies. The results show possible errors in the interpretation of validation studies as a consequence of the confusion of statistical concepts. To conclude, we made a proposal of “good practices” for the correct calculation, expression, and interpretation of the error when validating age estimation methods in forensic anthropology.

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Notes

  1. As a model is the result of applying a learning algorithm, the two terms sometimes used interchangeably. However, whenever more than one training sample is considered, it is important to remark that the same algorithm would produce different models, depending on which dataset is actually used for training.

  2. Other popular measures of the overall prediction error is the mean absolute error (MAE)

    $$ \text{MAE} = \frac{1}{n} \sum\limits_{i=1}^{n} | \epsilon_{i} | = \frac{1}{n} \sum\limits_{i=1}^{n} | Y_{i} - f(X_{i}) | $$

    Often, median, maximum, and minimum errors, either squared or absolute, are also reported. For simplicity, here we only use the MSE.

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Funding

This work has been supported by the Spanish Ministerio de Economía y Competitividad (MINECO) under the NEWSOCO project (ref. TIN2015-67661-P) and the Andalusian Dept. of Innovación, Ciencia y Empresa under the project TIC2011-7745, including European Regional Development Funds (ERDF-FEDER). Pablo Mesejo is funded by the European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship (grant number 746592).

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Authors and Affiliations

  1. Andalusian Research Institute in Data Science and Computational Intelligence, Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Andrea Valsecchi & Pablo Mesejo

  2. Laboratory of Anthropology, Department of Legal Medicine, Toxicology and Physical Anthropology, University of Granada, 18012, Granada, Spain

    Javier Irurita Olivares

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  1. Andrea Valsecchi
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  2. Javier Irurita Olivares
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Correspondence to Andrea Valsecchi.

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Valsecchi, A., Irurita Olivares, J. & Mesejo, P. Age estimation in forensic anthropology: methodological considerations about the validation studies of prediction models. Int J Legal Med 133, 1915–1924 (2019). https://doi.org/10.1007/s00414-019-02064-7

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  • DOI: https://doi.org/10.1007/s00414-019-02064-7

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