Given the high incidence of cardio and cerebrovascular diseases (CVD), and its association with morbidity and mortality, its prevention is a major public health issue. A high level of blood pressure is a well-known risk factor for these events and an increasing number of studies suggest that blood pressure variability may also be an independent risk factor. However, these studies suffer from significant methodological weaknesses. In this work we propose a new location-scale joint model for the repeated measures of a marker and competing events. This joint model combines a mixed model including a subject-specific and time-dependent residual variance modeled through random effects, and cause-specific proportional intensity models for the competing events. The risk of events may depend simultaneously on the current value of the variance, as well as, the current value and the current slope of the marker trajectory. The model is estimated by maximizing the likelihood function using the Marquardt-Levenberg algorithm. The estimation procedure is implemented in a R-package and is validated through a simulation study. This model is applied to study the association between blood pressure variability and the risk of CVD and death from other causes. Using data from a large clinical trial on the secondary prevention of stroke, we find that the current individual variability of blood pressure is associated with the risk of CVD and death. Moreover, the comparison with a model without heterogeneous variance shows the importance of taking into account this variability in the goodness-of-fit and for dynamic predictions.

翻译：心脑血管疾病因其高发病率及与患病率和死亡率的关联性，其预防已成为重大公共卫生问题。血压水平升高是这类事件的已知危险因素，且越来越多的研究表明血压变异性可能也是独立危险因子。然而，现有研究存在显著的方法学缺陷。本文提出一种新的位置-尺度联合模型，用于分析标志物重复测量数据与竞争事件之间的关系。该联合模型结合了包含随机效应建模的个体特异性时变残差方差的混合模型，以及针对竞争事件的原因特异性比例强度模型。事件风险可同时依赖于当前方差值、标志物轨迹的当前值及其当前斜率。通过使用Marquardt-Levenberg算法最大化似然函数来估计模型参数。该估计程序已在R语言包中实现，并通过模拟研究验证。我们将该模型应用于研究血压变异性与心脑血管疾病风险及非心血管原因死亡风险的关联。利用一项大型卒中二级预防临床试验数据，我们发现血压的当前个体变异度与心脑血管疾病及死亡风险相关。此外，与不含异质性方差模型的比较表明，在模型拟合优度评估和动态预测中考虑该变异性具有重要价值。