Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network.
Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network.
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DOI：
10.1177/09622802211070253
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发表时间：
2022-05
影响因子：
2.3
通讯作者：
Hawkins, Neil
中科院分区：
文献类型：
--
作者：
Freeman, Suzanne C.;Cooper, Nicola J.;Sutton, Alex J.;Crowther, Michael J.;Carpenter, James R.;Hawkins, Neil
从多项试验中综合临床有效性是决策的一个既定组成部分。通常使用考克斯比例风险模型（假设风险比随时间变化恒定）来综合至事件发生时间结局。然而，随着越来越多的试验报告治疗效果，其中风险比随时间变化，并且试验之间的随访时间长短不同，需要替代的综合方法。比较和对比五种模拟方法，以综合事件发生时间的结果，并就选择模拟方法的关键考虑因素提供指导。将考克斯比例风险模型和其他五种根据至事件发生时间结局估计治疗效果的方法（放宽比例风险假设）应用于报告总生存期的黑色素瘤试验网络：限制平均生存时间、广义伽马、分段指数、分数多项式和Royston-Parmar模型。所有模型都能很好地拟合黑素瘤网络。然而，在生存曲线的外推和建模约束的可解释性方面存在重要差异，这表明不同建模方法可能得出不同的结论。受限平均生存时间、广义伽马、分段指数、分数多项式和Royston-Parmar模型可在至事件发生时间结局的网络荟萃分析中适应非比例风险和不同的试验随访时间。我们建议使用可用和相关的先验知识、模型透明度、图形比较生存曲线和观察数据以帮助考虑生存估计值的可靠性以及考虑如何将治疗效果估计值纳入决策模型来告知模型选择。
Synthesis of clinical effectiveness from multiple trials is a well-established component of decision-making. Time-to-event outcomes are often synthesised using the Cox proportional hazards model assuming a constant hazard ratio over time. However, with an increasing proportion of trials reporting treatment effects where hazard ratios vary over time and with differing lengths of follow-up across trials, alternative synthesis methods are needed. To compare and contrast five modelling approaches for synthesis of time-to-event outcomes and provide guidance on key considerations for choosing between the modelling approaches. The Cox proportional hazards model and five other methods of estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption, were applied to a network of melanoma trials reporting overall survival: restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models. All models fitted the melanoma network acceptably well. However, there were important differences in extrapolations of the survival curve and interpretability of the modelling constraints demonstrating the potential for different conclusions from different modelling approaches. The restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can accommodate non-proportional hazards and differing lengths of trial follow-up within a network meta-analysis of time-to-event outcomes. We recommend that model choice is informed using available and relevant prior knowledge, model transparency, graphically comparing survival curves alongside observed data to aid consideration of the reliability of the survival estimates, and consideration of how the treatment effect estimates can be incorporated within a decision model.
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