Most often in genetic improvement studies, repeated measurements are observed on an individual animal, and these repeated measurements are often skewed. From the practical viewpoint, logarithm transformations of variables are usually adopted to reduce skewness, and this works satisfactorily in many cases. In most longitudinal datasets, however, because of the high rate of missingness, skewness often remains after transformation, the achievement of joint normality for each component of separately transformed variables, which are often difficult to interpret, is unrealistic. For this purpose, a more general form of distributions for considering skewness in the model should be used. In this paper, we used Bayesian joint modelling of longitudinal and survival data when data set presents skewness. A skew-normal mixed-effects model for longitudinal measurements and a Cox proportional hazard model for time to event variable were considered. We performed some simulation studies to investigate the performance of the proposed method to skewness in random effects, different dropout rates and sample sizes. Furthermore, we illustrated the proposed method using Nigerian indigenous chickens (NIC) dataset. The longitudinal outcomes of NIC data set were skewed, and presented left censored dropout. We assumed different model structures for the analysis of this data set and considered two versions of the deviance information criteria: namely, the conditional criteria (given the random effects) and marginal criteria (averaging over the random effects) in selecting the true model. These criteria were computed using the importance sampling method.

在遗传改良研究中，最常见的是在单个动物身上观察到重复测量，而这些重复测量通常是有偏差的。从实践的角度来看，通常采用变量的对数变换来减少偏度，这在许多情况下都令人满意。然而，在大多数纵向数据集中，由于缺失率高，偏度在转换后往往仍然存在，单独转换变量的每个分量的联合正态性的实现往往难以解释，这是不现实的。为此，应使用更通用的分布形式来考虑模型中的偏度。在本文中，当数据集呈现偏度时，我们使用了纵向和生存数据的贝叶斯联合建模。考虑了纵向测量的偏态正态混合效应模型和事件发生时间变量的 Cox 比例风险模型。我们进行了一些模拟研究，以研究所提出的方法对随机效应、不同辍学率和样本大小的偏度的性能。此外，我们使用尼日利亚本土鸡 (NIC) 数据集说明了所提出的方法。NIC 数据集的纵向结果是有偏差的，并呈现出左删失辍学。我们为该数据集的分析假设了不同的模型结构，并考虑了偏差信息标准的两个版本：即选择真实模型时的条件标准（给定随机效应）和边际标准（随机效应的平均值）。这些标准是使用重要性抽样方法计算的。