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A latent class approach for joint modeling of a time-to-event outcome and multiple longitudinal biomarkers subject to limits of detection

机译:一种潜在的阶级方法,用于联合建模的时间 - 事件结果和多种纵向生物标志物进行检测限制

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摘要

Multiple biomarkers on different biological pathways are often measured over time to investigate the complex mechanism of disease development and progression. Identification of informative subpopulation patterns of longitudinal biomarkers and clinical endpoint may assist in risk stratification and provide insights into new therapeutic targets. Motivated by a multicenter study to assess the inflammatory markers of sepsis in patients with community-acquired pneumonia, we propose a joint latent class analysis of multiple biomarkers and a time-to-event outcome while accounting for censored biomarker measurements due to detection limits. The interrelationship between biomarker trajectories and clinical endpoint is fully captured by a latent class structure, which reveals the subpopulation profiles of biomarkers and clinical outcome. The estimation of joint latent class models becomes more complicated when biomarkers are subject to detection limits. Based on a Metropolis-Hastings method, we develop a Monte Carlo Expectation-Maximization (MCEM) algorithm to estimate model parameters. We demonstrate the satisfactory performance of our MCEM algorithm using simulation studies, and apply our method to the motivating study to examine the heterogeneous patterns of cytokine responses to pneumonia and associated mortality risks.
机译:在不同的生物途径上的多种生物标志物通常随着时间的推移来测量,以研究疾病发展和进展的复杂机制。鉴定纵向生物标志物和临床终点的信息亚群模式可能有助于风险分层,并向新的治疗目标提供见解。由多中心研究的激励,评估患有社区肺炎患者的脓毒症的炎症标志,我们提出了多种生物标志物的联合潜在分析和时间 - 事件结果,同时考虑了由于检测限而被审查的生物标志物测量。生物标志物轨迹和临床终点之间的相互关系完全被潜在的阶级结构完全捕获,揭示了生物标志物的亚潜水层和临床结果。当生物标志物受到检测限制时,联合潜类模型的估计变得更加复杂。基于Metropolis-Hastings方法,我们开发了蒙特卡罗期望最大化(MCEM)算法来估算模型参数。我们使用模拟研究证明了我们的MCEM算法的令人满意的性能,并将方法应用于激励研究,以检查对肺炎和相关死亡率风险的细胞因子反应的异质模式。

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