Kazemi Iraj, Mahdiyeh Zahra, Mansourian Marjan, Park Jongbae J
Department of Statistics, College of Science, University of Isfahan, Iran.
Biom J. 2013 Jul;55(4):495-508. doi: 10.1002/bimj.201100208. Epub 2013 Apr 23.
Classical multivariate mixed models that acknowledge the correlation of patients through the incorporation of normal error terms are widely used in cohort studies. Violation of the normality assumption can make the statistical inference vague. In this paper, we propose a Bayesian parametric approach by relaxing this assumption and substituting some flexible distributions in fitting multivariate mixed models. This strategy allows for the skewness and the heavy tails of error-term distributions and thus makes inferences robust to the violation. This approach uses flexible skew-elliptical distributions, including skewed, fat, or thin-tailed distributions, and imposes the normal model as a special case. We use real data obtained from a prospective cohort study on the low back pain to illustrate the usefulness of our proposed approach.
通过纳入正态误差项来承认患者相关性的经典多元混合模型在队列研究中被广泛使用。违反正态性假设会使统计推断变得模糊。在本文中，我们通过放宽这一假设并在拟合多元混合模型时代入一些灵活的分布，提出了一种贝叶斯参数方法。这种策略考虑了误差项分布的偏度和厚尾性，从而使推断对违反假设具有稳健性。该方法使用灵活的偏态椭圆分布，包括偏态、厚尾或薄尾分布，并将正态模型作为一种特殊情况。我们使用从一项关于腰痛的前瞻性队列研究中获得的真实数据来说明我们提出的方法的实用性。
