Kearns Benjamin, Stevenson Matt D, Triantafyllopoulos Kostas, Manca Andrea
School of Health and Related Research, The University of Sheffield, Sheffield, England, UK.
School of Health and Related Research, The University of Sheffield, Sheffield, England, UK.
Value Health. 2021 Nov;24(11):1634-1642. doi: 10.1016/j.jval.2021.05.009. Epub 2021 Jul 28.
Curative treatments can result in complex hazard functions. The use of standard survival models may result in poor extrapolations. Several models for data which may have a cure fraction are available, but comparisons of their extrapolation performance are lacking. A simulation study was performed to assess the performance of models with and without a cure fraction when fit to data with a cure fraction.
Data were simulated from a Weibull cure model, with 9 scenarios corresponding to different lengths of follow-up and sample sizes. Cure and noncure versions of standard parametric, Royston-Parmar, and dynamic survival models were considered along with noncure fractional polynomial and generalized additive models. The mean-squared error and bias in estimates of the hazard function were estimated.
With the shortest follow-up, none of the cure models provided good extrapolations. Performance improved with increasing follow-up, except for the misspecified standard parametric cure model (lognormal). The performance of the flexible cure models was similar to that of the correctly specified cure model. Accurate estimates of the cured fraction were not necessary for accurate hazard estimates. Models without a cure fraction provided markedly worse extrapolations.
For curative treatments, failure to model the cured fraction can lead to very poor extrapolations. Cure models provide improved extrapolations, but with immature data there may be insufficient evidence to choose between cure and noncure models, emphasizing the importance of clinical knowledge for model choice. Dynamic cure fraction models were robust to model misspecification, but standard parametric cure models were not.
根治性治疗可能会导致复杂的风险函数。使用标准生存模型可能会导致外推效果不佳。有几种适用于可能存在治愈比例数据的模型，但缺乏对它们外推性能的比较。进行了一项模拟研究，以评估拟合存在治愈比例数据时，有治愈比例和无治愈比例模型的性能。
数据由威布尔治愈模型模拟生成，有9种情景，对应不同的随访时长和样本量。考虑了标准参数模型、罗伊斯顿-帕尔马模型和动态生存模型的治愈和非治愈版本，以及非治愈分数多项式模型和广义相加模型。估计了风险函数估计值的均方误差和偏差。
随访时间最短时，没有一个治愈模型能提供良好的外推。除了指定错误的标准参数治愈模型（对数正态）外，随着随访时间增加，性能有所改善。灵活的治愈模型的性能与指定正确的治愈模型相似。准确估计治愈比例对于准确估计风险并非必要。没有治愈比例的模型外推效果明显更差。
对于根治性治疗，未能对治愈比例进行建模可能会导致外推效果非常差。治愈模型能提供更好的外推，但对于不成熟的数据，可能没有足够的证据在治愈模型和非治愈模型之间做出选择，这凸显了临床知识对于模型选择的重要性。动态治愈比例模型对模型指定错误具有鲁棒性，但标准参数治愈模型则不然。
