重复测量和事件发生时间数据的贝叶斯分位数联合建模，应用于囊性纤维化患者的肺功能下降和感染时间
Bayesian Quantile-Based Joint Modelling of Repeated Measurement and Time-to-Event data, with an Application to Lung Function Decline and Time to Infection in Patients with Cystic Fibrosis
摘要：
背景资料：重复测量和事件发生时间数据联合建模的最广泛使用的方法是将重复测量的线性高斯随机效应模型与事件发生时间结果的对数高斯脆弱性模型相结合，通过随机效应和对数脆弱性之间的某种形式的相关结构将两者联系起来。在这种方法中，假设协变量影响重复测量数据的平均响应曲线。目标：有些应用程序提出了本结构无法涵盖的实质性问题。例如，囊性纤维化（CF）研究中的一个重要问题是了解患者的肺功能轨迹对其获得各种感染的风险的影响，以及这在肺功能分布的不同分位数下如何变化。研究方法：出于这个问题，我们开发了一个联合分位数建模框架，在本文中与相关的马尔可夫链蒙特卡罗算法的贝叶斯推断。结果：从共同关节模型到分位数回归的转换成功，并应用于来自英国的CF数据。该方法有助于检测不同分位数中肺功能下降与感染发作之间关系的总体差异。结论：不考虑特殊异方差结构的联合建模在某些研究问题中是不够的，需要向均值以外的模型扩展。
英文摘要：
Background: The most widely used approach to joint modelling of repeated measurement and time to event data is to combine a linear Gaussian random effects model for the repeated measurements with a log-Gaussian frailty model for the time-to-event outcome, linking the two through some form of correlation structure between the random effects and the log-frailty. In this approach, covariates are assumed to affect the mean response profile of the repeated measurement data. Objectives: Some applications raise substantive questions that cannot be captured by this structure. For example, an important question in cystic fibrosis (CF) research is to understand the impact of a patient's lung function trajectory on their risk of acquiring a variety of infections, and how this varies at different quantiles of the lung function distribution. Methods: Motivated by this question, we develop a joint quantile modelling framework in this paper with an associated Markov Chain Monte Carlo algorithm for Bayesian inference. Results: The translation from the common joint model towards quantile regression succeeds and is applied to CF data from the United Kingdom. The method helps detecting an overall difference in the relation between lung function decline and onset of infection in the different quantiles. Conclusions: Joint modelling without taking into account the special heteroscedastic structure is not sufficient in certain research question and the extensions towards models beyond the mean is necessary.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
