BACKGROUND The recent progress in medical research generates an increasing interest in the use of longitudinal biomarkers for characterizing the occurrence of an outcome. The present work is motivated by a study, where the objective was to explore the potential of the long pentraxin 3 (PTX3) as a prognostic marker of Acute Graft-versus-Host Disease (GvHD) after haematopoietic stem cell transplantation. Time-varying covariate Cox model was commonly used, despite its limiting assumptions that marker values are constant in time and measured without error. A joint model has been developed as a viable alternative; however, the approach is computationally intensive and requires additional strong assumptions, in which the impacts of their misspecification were not sufficiently studied. METHODS We conduct an extensive simulation to clarify relevant assumptions for the understanding of joint models and assessment of its robustness under key model misspecifications. Further, we characterize the extent of bias introduced by the limiting assumptions of the time-varying covariate Cox model and compare its performance with a joint model in various contexts. We then present results of the two approaches to evaluate the potential of PTX3 as a prognostic marker of GvHD after haematopoietic stem cell transplantation. RESULTS Overall, we illustrate that a joint model provides an unbiased estimate of the association between a longitudinal marker and the hazard of an event in the presence of measurement error, showing improvement over the time-varying Cox model. However, a joint model is severely biased when the baseline hazard or the shape of the longitudinal trajectories are misspecified. Both the Cox model and the joint model correctly specified indicated PTX3 as a potential prognostic marker of GvHD, with the joint model providing a higher hazard ratio estimate. CONCLUSIONS Joint models are beneficial to investigate the capability of the longitudinal marker to characterize time-to-event endpoint. However, the benefits are strictly linked to the correct specification of the longitudinal marker trajectory and the baseline hazard function, indicating a careful consideration of assumptions to avoid biased estimates.

中文翻译：

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将联合模型的鲁棒性与时变协变量Cox模型进行比较，以评估纵向标记和事件发生时间终点之间的关联。

背景技术医学研究中的最新进展引起人们对使用纵向生物标志物表征结果发生的兴趣日益增长。这项研究的目的是为了研究造血干细胞移植后长五味子蛋白3（PTX3）作为急性移植物抗宿主病（GvHD）的预后标志物的潜力。尽管时变协变量Cox模型的局限性在于标记值在时间上是恒定的并且没有错误地进行测量，但还是经常使用。已经开发了联合模型作为可行的替代方案。但是，该方法需要大量的计算，并且需要额外的强有力的假设，在这些假设中，对错误指定的影响没有得到足够的研究。方法我们进行了广泛的模拟，以澄清相关假设，以理解联合模型并评估在关键模型错误指定下的鲁棒性。此外，我们表征了时变协变量Cox模型的局限性假设所引入的偏差程度，并在各种情况下将其性能与联合模型进行了比较。然后，我们介绍两种方法的结果，以评估PTX3作为造血干细胞移植后GvHD的预后标志物的潜力。结果总的来说，我们说明了联合模型提供了对纵向标记和存在测量误差的事件危险之间关联的无偏估计，显示了随时间变化的Cox模型的改进。然而，当基线危险或纵向轨迹的形状指定不正确时，关节模型将严重偏倚。正确指定的Cox模型和关节模型都将PTX3指示为GvHD的潜在预后标志物，而关节模型则提供了更高的危险比估算值。结论联合模型有利于研究纵向标记物表征事件至事件终点的能力。但是，这些好处与纵向标记轨迹的正确规范和基线危险函数严格相关，这表明要仔细考虑各种假设，以免产生偏差。联合模型可提供更高的风险比估算值。结论联合模型有利于研究纵向标记物表征事件至事件终点的能力。但是，这些好处与纵向标记轨迹的正确规范和基线危险函数严格相关，这表明要仔细考虑各种假设，以免产生偏差。联合模型可提供更高的风险比估算值。结论联合模型有利于研究纵向标记物表征事件至事件终点的能力。但是，这些好处与纵向标记轨迹的正确规范和基线危险函数严格相关，这表明要仔细考虑各种假设，以免产生偏差。