使用患者登记数据的(非)有界纵向标记、竞争风险和复发事件的联合模型。

IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Pedro Miranda Afonso, Dimitris Rizopoulos, Anushka K Palipana, Emrah Gecili, Cole Brokamp, John P Clancy, Rhonda D Szczesniak, Eleni-Rosalina Andrinopoulou
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引用次数: 0

摘要

纵向和生存数据的联合模型已成为研究反复测量的生物标志物与临床事件之间关系的流行框架。然而,处理复杂的生存数据结构,特别是处理单个模型中的重复事件和竞争事件时间,仍然是一个挑战。这会导致重要的信息被忽略。此外,现有框架依赖于连续标记的高斯分布,这可能不适合有界生物标记,导致关联估计有偏差。为了解决这些限制,我们提出了一个贝叶斯共享参数联合模型,该模型同时容纳多个(可能有界的)纵向标记,一个循环事件过程和竞争风险。我们使用beta分布来对任何区间(a, b) $$ \left(a,b\right) $$内的响应进行建模,而不牺牲关联的可解释性。该模型提供了各种形式的关联、不连续的风险间隔,以及间隙和日历时间尺度。仿真研究表明,该模型优于简单的关节模型。我们利用美国囊性纤维化基金会患者登记来研究肺功能和体重指数变化与复发性肺恶化风险之间的关系,同时考虑死亡和肺移植的竞争风险。我们的高效实现允许快速拟合模型,尽管它的复杂性和该患者注册的大样本量。我们的综合方法通过比以前更精确地量化最重要的临床标志物和事件之间的关系,为囊性纤维化疾病的进展提供了新的见解。模型实现可以在R包JMbayes2中获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Joint Model for (Un)Bounded Longitudinal Markers, Competing Risks, and Recurrent Events Using Patient Registry Data.

Joint models for longitudinal and survival data have become a popular framework for studying the association between repeatedly measured biomarkers and clinical events. Nevertheless, addressing complex survival data structures, especially handling both recurrent and competing event times within a single model, remains a challenge. This causes important information to be disregarded. Moreover, existing frameworks rely on a Gaussian distribution for continuous markers, which may be unsuitable for bounded biomarkers, resulting in biased estimates of associations. To address these limitations, we propose a Bayesian shared-parameter joint model that simultaneously accommodates multiple (possibly bounded) longitudinal markers, a recurrent event process, and competing risks. We use the beta distribution to model responses bounded within any interval ( a , b ) $$ \left(a,b\right) $$ without sacrificing the interpretability of the association. The model offers various forms of association, discontinuous risk intervals, and both gap and calendar timescales. A simulation study shows that it outperforms simpler joint models. We utilize the US Cystic Fibrosis Foundation Patient Registry to study the associations between changes in lung function and body mass index, and the risk of recurrent pulmonary exacerbations, while accounting for the competing risks of death and lung transplantation. Our efficient implementation allows fast fitting of the model despite its complexity and the large sample size from this patient registry. Our comprehensive approach provides new insights into cystic fibrosis disease progression by quantifying the relationship between the most important clinical markers and events more precisely than has been possible before. The model implementation is available in the R package JMbayes2.

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来源期刊
Statistics in Medicine
Statistics in Medicine 医学-公共卫生、环境卫生与职业卫生
CiteScore
3.40
自引率
10.00%
发文量
334
审稿时长
2-4 weeks
期刊介绍: The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.
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