ABSTRACT

Joint modeling of longitudinal measurements and time-to-event data is used in many practical studies of medical sciences. Most of the time, particularly in clinical studies and health inquiry, there are more than one event and they compete for failing an individual. In this situation, assessing the competing risk failure time is important. In most cases, implementation of joint modeling involves complex calculations. Therefore, we propose a two-stage method for joint modeling of longitudinal measurements and competing risks (JMLC) data based on the full likelihood approach via the conditional EM (CEM) algorithm. In the first stage, a linear mixed effect model is used to estimate the parameters of the longitudinal sub-model. In the second stage, we consider a cause-specific sub-model to construct competing risks data and describe an approximation for the joint log-likelihood that uses the estimated parameters of the first stage. We express the results of a simulation study and perform this method on the “standard and new anti-epileptic drugs” trial to check the effect of drug assaying on the treatment effects of lamotrigine and carbamazepine through treatment failure.

摘要

纵向测量和时间到事件数据的联合建模用于医学科学的许多实际研究。大多数时候，特别是在临床研究和健康调查中，有不止一个事件，它们会争夺一个人的失败。在这种情况下，评估竞争风险故障时间很重要。在大多数情况下，联合建模的实现涉及复杂的计算。因此，我们提出了一种基于全似然方法通过条件 EM (CEM) 算法对纵向测量和竞争风险 (JMLC) 数据进行联合建模的两阶段方法。在第一阶段，使用线性混合效应模型来估计纵向子模型的参数。在第二阶段，我们考虑特定原因的子模型来构建竞争风险数据，并描述使用第一阶段估计参数的联合对数似然的近似值。我们表达了模拟研究的结果，并在“标准和新的抗癫痫药物”试验中执行该方法，以检查药物测定对拉莫三嗪和卡马西平治疗失败的治疗效果的影响。