发表机构
Basque Center for Applied Mathematics BCAM; University of the Basque Country UPV/EHU; MRC Biostatistics Unit, University of Cambridge; IE University（巴斯克应用数学中心; 巴斯克大学; 剑桥大学医学研究委员会生物统计学单位; IE大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出贝叶斯联合模型，结合β-二项与Weibull子模型，同时估计纵向PROs与生存，减少偏差，在543名慢阻肺患者中识别更多关联并提供动态预测。
AI 中文摘要
基于问卷的患者报告结局（PROs）是离散、有界且过度离散的，然而将它们与生存关联的联合模型可能忽略这些特征，或顺序地估计两个过程。我们提出了一种贝叶斯联合模型，将β-二项混合效应子模型与Weibull比例风险子模型相结合，通过受试者特定的响应概率进行关联。模拟表明，与两阶段估计相比，同时估计减少了纵向斜率的偏差，并产生了几乎无偏的关联估计。在一个包含543名慢性阻塞性肺疾病患者的队列中，该模型识别了所有八个SF-36维度以及三个SGRQ维度中的两个维度的关联，包括两阶段方法未检测到的若干关联，并提供了动态生存预测。
英文摘要
Questionnaire-based patient-reported outcomes (PROs) are discrete, bounded and overdispersed, yet joint models relating them to survival may ignore these features or estimate both processes sequentially. We propose a Bayesian joint model combining a beta-binomial mixed-effects submodel with a Weibull proportional hazards submodel, linked through the subject-specific response probability. Simulations show that simultaneous estimation reduces bias in the longitudinal slope and yields practically unbiased association estimates, unlike two-stage estimation. In a cohort of 543 patients with chronic obstructive pulmonary disease, the model identified associations for all eight SF-36 dimensions and for two of three SGRQ dimensions, including several associations not detected by the two-stage approach, and provided dynamic survival predictions.
