BACKGROUND Prognostic models are becoming increasingly relevant in clinical trials as potential surrogate endpoints, and for patient management as clinical decision support tools. However, the impact of competing risks on model performance remains poorly investigated. We aimed to carefully assess the performance of competing risk and noncompeting risk models in the context of kidney transplantation, where allograft failure and death with a functioning graft are two competing outcomes. METHODS We included 11,046 kidney transplant recipients enrolled in 10 countries. We developed prediction models for long-term kidney graft failure prediction, without accounting (i.e., censoring) and accounting for the competing risk of death with a functioning graft, using Cox, Fine-Gray, and cause-specific Cox regression models. To this aim, we followed a detailed and transparent analytical framework for competing and noncompeting risk modelling, and carefully assessed the models' development, stability, discrimination, calibration, overall fit, clinical utility, and generalizability in external validation cohorts and subpopulations. More than 15 metrics were used to provide an exhaustive assessment of model performance. RESULTS Among 11,046 recipients in the derivation and validation cohorts, 1,497 (14%) lost their graft and 1,003 (9%) died with a functioning graft after a median follow-up post-risk evaluation of 4.7 years (IQR 2.7-7.0). The cumulative incidence of graft loss was similarly estimated by Kaplan-Meier and Aalen-Johansen methods (17% versus 16% in the derivation cohort). Cox and competing risk models showed similar and stable risk estimates for predicting long-term graft failure (average mean absolute prediction error of 0.0140, 0.0138 and 0.0135 for Cox, Fine-Gray, and cause-specific Cox models, respectively). Discrimination and overall fit were comparable in the validation cohorts, with concordance index ranging from 0.76 to 0.87. Across various subpopulations and clinical scenarios, the models performed well and similarly, although in some high-risk groups (such as donors over 65 years old), the findings suggest a trend towards moderately improved calibration when using a competing risk approach. CONCLUSIONS Competing and noncompeting risk models performed similarly in predicting long-term kidney graft failure.

中文翻译：

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预测同种异体肾移植失败的竞争和非竞争风险模型。


背景 预后模型在临床试验中作为潜在的替代终点变得越来越重要，作为临床决策支持工具用于患者管理。然而，竞争风险对模型性能的影响仍然研究不足。我们旨在仔细评估竞争风险和非竞争风险模型在肾移植情况下的表现，其中同种异体移植失败和功能性移植物死亡是两个竞争结局。方法 我们纳入了 11,046 个国家/地区的 10 名肾移植受者。我们使用 Cox、Fine-Gray 和原因特异性 Cox 回归模型开发了用于长期肾移植失败预测的预测模型，无需考虑（即删失）并考虑功能性移植物的竞争性死亡风险。为此，我们遵循了详细而透明的竞争和非竞争风险建模分析框架，并仔细评估了模型在外部验证队列和亚群中的开发、稳定性、区分度、校准、整体拟合、临床效用和泛化性。使用了超过 15 个指标来提供对模型性能的详尽评估。结果 在衍生和验证队列的 11,046 名受者中，1,497 名 （14%） 失去了移植物，1,003 名 （9%） 在中位随访风险评估后 4.7 年 （IQR 2.7-7.0） 后死于功能性移植物。移植物损失的累积发生率同样通过 Kaplan-Meier 和 Aalen-Johansen 方法估计 （17% 对 16% 派生队列）。Cox 和竞争风险模型在预测长期移植物失败方面显示出相似且稳定的风险估计（平均平均绝对预测误差为 0.0140、0.0138 和 0.0135 分别用于 Cox、Fine-Gray 和原因特异性 Cox 模型）。验证队列中的辨别力和总体拟合度相当，一致性指数范围为 0.76 至 0.87。在各种亚群和临床场景中，模型表现良好且相似，尽管在一些高危群体（例如 65 岁以上的供体）中，研究结果表明，当使用竞争风险方法时，校准有适度改进的趋势。结论 竞争和非竞争风险模型在预测长期肾移植失败方面的表现相似。