Cheverko Colleen M, Hubbe Mark
Department of Anthropology, The Ohio State University, Columbus, Ohio.
Instituto de Investigaciones Arqueológicas y Museo, Universidad Católica del Norte, Chile.
Am J Phys Anthropol. 2017 Jun;163(2):407-416. doi: 10.1002/ajpa.23206. Epub 2017 Mar 20.
Many authors argue that inconsistencies between studies of skeletal markers are based on different data collection protocols, especially when comparing age-related markers such as osteoarthritis. Less attention is given to the choice of statistical techniques that are used to test the hypotheses associated with the data. This paper addresses how different statistical techniques compare the prevalence of age-related skeletal indicators, specifically osteoarthritis.
Osteoarthritis prevalence was scored in eight postcranial joints in 243 adult individuals from seven prehistoric archaeological sites in Central California, and data was compared between three time periods [Early (4800-2800 BP), Middle (2800-1200 BP), and Late (1200-250 BP)] using commonly used statistical tests: chi-square, Fisher's exact, and odds ratios. In addition, we analyzed the data with tests that are able to take into consideration the effect of age on osteoarthritis prevalence: ANCOVA and Factorial ANOVA. Finally, bootstraps were applied to the data to investigate how fluctuating frequencies, sample size, and age-at-death distributions affected the interpretations resulting from each test.
The results demonstrate that the tests that consider age as a covariate (ANCOVA and Factorial ANOVA) are more efficient in rejecting the null hypothesis when smaller magnitudes of difference are observed between samples, irrespective of sample size, even though osteoarthritis prevalence fails to meet assumptions of normal distribution and homoscedasticity.
ANCOVAs or Factorial ANOVAs that incorporate age as a covariate should be considered more often in studies that test different prevalences of age-related osteological markers among past populations.
许多作者认为,骨骼标志物研究之间的不一致是基于不同的数据收集方案,特别是在比较与年龄相关的标志物(如骨关节炎)时。用于检验与数据相关假设的统计技术的选择则较少受到关注。本文探讨了不同的统计技术如何比较与年龄相关的骨骼指标(特别是骨关节炎)的患病率。
对来自加利福尼亚中部七个史前考古遗址的243名成年人的八个颅后关节的骨关节炎患病率进行评分,并使用常用的统计检验(卡方检验、费舍尔精确检验和优势比)对三个时间段[早期(公元前4800 - 2800年)、中期(公元前2800 - 1200年)和晚期(公元前1200 - 250年)]的数据进行比较。此外,我们使用能够考虑年龄对骨关节炎患病率影响的检验方法(协方差分析和析因方差分析)对数据进行分析。最后,对数据应用自助法,以研究频率波动、样本量和死亡年龄分布如何影响每种检验的结果解释。
结果表明,当样本之间观察到较小的差异幅度时,无论样本量大小,将年龄作为协变量的检验方法(协方差分析和析因方差分析)在拒绝原假设方面更有效,尽管骨关节炎患病率不符合正态分布和同方差性的假设。
在检验过去人群中与年龄相关的骨学标志物不同患病率的研究中,应更频繁地考虑将年龄作为协变量的协方差分析或析因方差分析。