Ferrante Luigi, Skrami Edlira, Gesuita Rosaria, Cameriere Roberto
Center of Epidemiology, Biostatistics and Medical Information Technology, Department of Biomedical Sciences and Public Health, School of Medicine, Polytechnic University of Marche, 60020, Torrette di Ancona, Italy.
Stat Med. 2015 May 10;34(10):1779-90. doi: 10.1002/sim.6448. Epub 2015 Feb 2.
Forensic medicine is increasingly called upon to assess the age of individuals. Forensic age estimation is mostly required in relation to illegal immigration and identification of bodies or skeletal remains. A variety of age estimation methods are based on dental samples and use of regression models, where the age of an individual is predicted by morphological tooth changes that take place over time. From the medico-legal point of view, regression models, with age as the dependent random variable entail that age tends to be overestimated in the young and underestimated in the old. To overcome this bias, we describe a new full Bayesian calibration method (asymmetric Laplace Bayesian calibration) for forensic age estimation that uses asymmetric Laplace distribution as the probability model. The method was compared with three existing approaches (two Bayesian and a classical method) using simulated data. Although its accuracy was comparable with that of the other methods, the asymmetric Laplace Bayesian calibration appears to be significantly more reliable and robust in case of misspecification of the probability model. The proposed method was also applied to a real dataset of values of the pulp chamber of the right lower premolar measured on x-ray scans of individuals of known age.
法医学越来越多地被要求评估个体的年龄。法医年龄估计主要用于非法移民以及尸体或骨骼遗骸的身份鉴定。多种年龄估计方法基于牙齿样本并使用回归模型，通过随时间发生的形态学牙齿变化来预测个体的年龄。从法医学角度来看，以年龄为因随机变量的回归模型往往会导致年轻人的年龄被高估，而老年人的年龄被低估。为克服这种偏差，我们描述了一种用于法医年龄估计的全新全贝叶斯校准方法（非对称拉普拉斯贝叶斯校准），该方法使用非对称拉普拉斯分布作为概率模型。使用模拟数据将该方法与三种现有方法（两种贝叶斯方法和一种经典方法）进行了比较。尽管其准确性与其他方法相当，但在概率模型设定错误的情况下，非对称拉普拉斯贝叶斯校准似乎明显更可靠且稳健。所提出的方法还应用于一个真实数据集，该数据集是对已知年龄个体的右下前磨牙牙髓腔在X光扫描上测量的值。
