Chronic obstructive pulmonary disease (COPD) is a chronic obstructive disease of the airways. An exacerbation of COPD is defined as shortness of breath, cough, and sputum production. New therapies for COPD exacerbations are examined in clinical trials frequently based on the number of exacerbations that implies long-term study due to the high variability in occurrence and duration of the events. In this work, we expanded the two-state model developed by Cook et al. where the patient transits from an asymptomatic (state 1) to a symptomatic state (state 2) and vice versa, through investigating different semi-Markov models in a Bayesian context using data from actual clinical trials. Of the four models tested, the log-logistic model was shown to adequately characterize the duration and number of COPD exacerbations. The patient disease stage was found a significant covariate with an effect of accelerating the transition from asymptomatic to symptomatic state. In addition, the best dropout model (log-logistic) was incorporated in the final two-state model to describe the dropout mechanism. Simulation based diagnostics such as posterior predictive check (PPC) and visual predictive check (VPC) were used to assess the behaviour of the model. The final model was applied in three clinical trial data to investigate its ability to detect the drug effect: the drug effect was captured in all three datasets and in both directions (from state 1 to state 2 and vice versa). A practical design investigation was also carried out and showed the limits of reducing the number of subjects and study length on the drug effect identification. Finally, clinical trial simulation confirmed that the model can potentially be used to predict medium term (6–12 months) clinical trial outcome using the first 3 months data, but at the expense of showing a non-significant drug effect.

中文翻译：

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利用贝叶斯方法研究COPD加重的两种状态的混合模型。

慢性阻塞性肺疾病（COPD）是气道的慢性阻塞性疾病。COPD恶化定义为呼吸急促，咳嗽和痰液分泌。由于事件发生和持续时间的高变异性，在临床试验中经常根据可能导致长期研究的加重次数来检查用于COPD加重的新疗法。在这项工作中，我们扩展了Cook等人开发的两态模型。通过使用来自实际临床试验的数据在贝叶斯背景下研究不同的半马尔可夫模型，患者将从无症状（状态1）转变为有症状状态（状态2），反之亦然。在所测试的四个模型中，对数逻辑模型显示出足以表征COPD恶化的持续时间和次数。发现患者疾病阶段具有显着的协变量，并具有加速从无症状状态过渡到有症状状态的作用。另外，最佳的辍学模型（对数逻辑）被合并到最终的两种状态模型中，以描述辍学机制。基于仿真的诊断（例如后验预测检查（PPC）和视觉预测检查（VPC））用于评估模型的行为。将最终模型应用于三个临床试验数据中，以研究其检测药物作用的能力：在所有三个数据集中并在两个方向（从状态1到状态2，反之亦然）捕获了药物作用。还进行了一项实用的设计调查，结果表明在减少药物效应的过程中，减少受试者人数和研究时间的局限性。最后，