Centre for Quantitative Medicine, Office of Clinical Sciences, Duke-NUS Graduate Medical School, Singapore; Scientific Development Division, Singapore Clinical Research Institute, Singapore.
Stat Med. 2014 Jun 15;33(13):2263-74. doi: 10.1002/sim.6093. Epub 2014 Jan 22.
Recurrent event data with a fraction of subjects having zero event are often seen in randomized clinical trials. Those with zero event may belong to a cured (or non-susceptible) fraction. Event dependence refers to the situation that a person's past event history affects his future event occurrences. In the presence of event dependence, an intervention may have an impact on the event rate in the non-cured through two pathways-a primary effect directly on the outcome event and a secondary effect mediated through event dependence. The primary effect combined with the secondary effect is the total effect. We propose a frailty mixture model and a two-step estimation procedure for the estimation of the effect of an intervention on the probability of cure and the total effect on event rate in the non-cured. A summary measure of intervention effects is derived. The performance of the proposed model is evaluated by simulation. Data on respiratory exacerbations from a randomized, placebo-controlled trial are re-analyzed for illustration.
在随机临床试验中，经常会出现部分受试者的事件发生率为零的重复事件数据。那些没有事件发生的人可能属于治愈（或不易感）的部分。事件依赖性是指一个人过去的事件历史会影响他未来的事件发生。在存在事件依赖性的情况下，干预措施可能会通过两种途径对未治愈者的事件发生率产生影响——一种是对结局事件的直接主要影响，另一种是通过事件依赖性的中介影响。主要影响与次要影响相结合就是总影响。我们提出了一种脆弱性混合模型和两步估计程序，用于估计干预对治愈率的影响以及对未治愈者事件发生率的总影响。得出了干预效果的综合度量。通过模拟评估了所提出模型的性能。为了说明问题，重新分析了一项随机、安慰剂对照试验中关于呼吸恶化的结果数据。
