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International Journal of Legal Medicine

, Volume 133, Issue 6, pp 1915–1924 | Cite as

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

  • Andrea ValsecchiEmail author
  • Javier Irurita Olivares
  • Pablo Mesejo
Original Article

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.

Keywords

Age estimation Regression problems Methodological review Validation studies 

Notes

Funding information

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).

References

  1. 1.
    Christensen AM, Crowder CM (2009) Evidentiary standards for forensic anthropology*. J Forensic Sci 54 (6):1211–1216PubMedGoogle Scholar
  2. 2.
    De Luca S, Navarro F, Cameriere R (2013) The expert witness and its admissibility as evidence in the Spanish legal system. Revista Electrónica de Ciencia Penal y Criminología 15–19:19:1–19:14. [Online; http://criminet.ugr.es/recpc/15/recpc15-19.pdf]Google Scholar
  3. 3.
    Franklin D (2010) Forensic age estimation in human skeletal remains: current concepts and future directions. Leg Med 12(1):1–7Google Scholar
  4. 4.
    Márquez-Grant N (2015) An overview of age estimation in forensic anthropology: perspectives and practical considerations. Ann Hum Biol 42(4):308–322PubMedGoogle Scholar
  5. 5.
    Lesciotto KM (2015) The impact of daubert on the admissibility of forensic anthropology expert testimony. J Forensic Sci 60(3):549–555PubMedGoogle Scholar
  6. 6.
    Cunha E, Baccino E, Martrille L, Ramsthaler F, Prieto J, Schuliar Y, Lynnerup N, Cattaneo C (2009) The problem of aging human remains and living individuals: a review. Forensic Sci Int 193(1):1–13Google Scholar
  7. 7.
    Buckberry J (2015) The (mis)use of adult age estimates in osteology. Ann Hum Biol 42(4):323–331PubMedGoogle Scholar
  8. 8.
    Kimmerle EH, Jantz RL (2008) Variation as evidence: introduction to a symposium on international human identification. J Forensic Sci 53(3):521–523PubMedGoogle Scholar
  9. 9.
    Kimmerle EH, Jantz RL, Konigsberg LW, Baraybar JP (2008) Skeletal estimation and identification in american and east european populations*. J Forensic Sci 53(3):524–532PubMedGoogle Scholar
  10. 10.
    Konigsberg LW, Herrmann NP, Wescott DJ, Kimmerle EH (2008) Estimation and evidence in forensic anthropology: age-at-death. J Forens Sci 53(3):541–557Google Scholar
  11. 11.
    Liversidge HM (2010) Interpreting group differences using Demirjian’s dental maturity method. Forensic Sci Int 201(1):95–101PubMedGoogle Scholar
  12. 12.
    Liversidge HM (2015) Controversies in age estimation from developing teeth. Ann Hum Biol 42(4):397–406PubMedGoogle Scholar
  13. 13.
    Ubelaker DH (2008) Issues in the global applications of methodology in forensic anthropology*. J Forensic Sci 53(3):606–607PubMedGoogle Scholar
  14. 14.
    Corron L, Marchal F, Condemi S, Adalian P (2018) A critical review of sub-adult age estimation in biological anthropology: do methods comply with published recommendations? Forensic Science InternationalGoogle Scholar
  15. 15.
    Konigsberg LW (2015) Multivariate cumulative probit for age estimation using ordinal categorical data. Ann Hum Biol 42(4):368–378PubMedGoogle Scholar
  16. 16.
    Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Statist Soc Series B (Methodological), 267–288Google Scholar
  17. 17.
    Prechelt L (1998) Early stopping-but when? In: Neural networks: tricks of the trade, this book is an outgrowth of a 1996 NIPS workshop, pp 55–69Google Scholar
  18. 18.
    Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958Google Scholar
  19. 19.
    Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140Google Scholar
  20. 20.
    Kohavi R, et al. (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Ijcai, vol 14, pp 1137–1145Google Scholar
  21. 21.
    Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30Google Scholar
  22. 22.
    Wilcoxon F (1945) Individual comparisons by ranking methods. Biometr Bull 1(6):80–83Google Scholar
  23. 23.
    Kuczmarski RJ, Ogden CL, Guo SS, Grummer-Strawn LM, Flegal KM, Mei Z, Wei R, Curtin LR, Roche AF, Johnson CL (2002) 2000 CDC growth charts for the United States: methods and development. Vital and health statistics. Series 11, Data from the National Health Survey 246:1–190Google Scholar
  24. 24.
    Aykroyd RG, Lucy D, Pollard AM, Solheim T (1997) Technical note: regression analysis in adult age estimation. Am J Phys Anthropol 104(2):259–265Google Scholar
  25. 25.
    Cleveland WS, Devlin SJ (1988) Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc 83(403):596–610Google Scholar
  26. 26.
    Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844Google Scholar
  27. 27.
    Jayaraman J, Wong HM, King NM, Roberts GJ (2013) The french–canadian data set of Demirjian for dental age estimation: a systematic review and meta-analysis. J Forens Legal Med 20(5):373–381Google Scholar
  28. 28.
    Yaşar Işcan M, Loth SR, Wright RK (1984) Metamorphosis at the sternal rib end: a new method to estimate age at death in white males. Am J Phys Anthropol 65(2):147–156Google Scholar
  29. 29.
    Brooks S, Suchey JM (1990) Skeletal age determination based on the os pubis: a comparison of the Acsádi-Nemeskéri and Suchey-Brooks methods. Human Evol 5(3):227–238Google Scholar
  30. 30.
    Lamendin H, Baccino E, Humbert JF, Tavernier JC, Nossintchouk RM, Zerilli A (1992) A simple technique for age estimation in adult corpses: the two criteria dental method. J Forens Sci 37(5):1373–1379Google Scholar
  31. 31.
    Cardoso HFV, Vandergugten JM, Humphrey LT (2017) A ge estimation of immature human skeletal remains from the metaphyseal and epiphyseal widths of the long bones in the post-natal period. Am J Phys Anthropol 162 (1):19–35PubMedGoogle Scholar
  32. 32.
    Olivares JI, Aguilera IA (2017) Proposal of new regression formulae for the estimation of age in infant skeletal remains from the metric study of the pars Basilaris. Int J Legal Med 131(3):781–788Google Scholar
  33. 33.
    Olivares JI, Aguilera IA, Badal JV, De Luca S, López MCB (2014) Evaluation of the maximum length of deciduous teeth for estimation of the age of infants and young children: proposal of new regression formulas. Int J Legal Med 128(2):345– 352Google Scholar
  34. 34.
    Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Statist, 65–70Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Andalusian Research Institute in Data Science and Computational Intelligence, Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  2. 2.Laboratory of Anthropology, Department of Legal Medicine, Toxicology and Physical AnthropologyUniversity of GranadaGranadaSpain

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