AI 中文总结
本研究提出统一参数框架，结合随访充分性检验与两种容忍度准则，通过模拟验证性能并应用于癌症数据，实现治愈模型中随访充分性评估与所需时间估计的统一。
AI 中文摘要
可靠的治愈分数估计关键依赖于充足的随访。经典的随访充分性评估程序主要是推断性的，而另一部分文献则使用超额风险、条件治愈概率或剩余生存定义来估计治愈时间。这两种方法在很大程度上是脱节的：评估观察到的随访充分性通常不会在明确的容忍度下量化所需的额外随访时间，而估计治愈时间本身也不会对可用随访提供正式的推断性评估。我们开发了一个统一的参数框架，该框架将参数随访充分性检验（比较Kaplan-Meier终点估计值与参数混合治愈模型估计的治愈分数）与两种互补的基于容忍度的公式相结合：总体生存尺度上的平台距离准则和易感生存尺度上的剩余生存准则。我们在Weibull混合治愈模型下推导了这两种准则的闭式表达式，并确定在其容忍度参数的适当变换下，它们产生相同的最小随访时间。我们通过在不同样本量、治愈分数和行政删失场景下的蒙特卡洛模拟评估了该框架的有限样本性能。对前列腺癌和三阴性乳腺癌数据的应用表明，该框架如何评估可用随访、在预先指定的容忍度下估计额外观察时间，以及识别随访已充足的场景。
英文摘要
Reliable estimation of cure fractions depends critically on adequate follow-up. Classical procedures for assessing follow-up sufficiency are primarily inferential, whereas a separate body of literature estimates time to cure using excess-hazard, conditional cure-probability, or residual-survival definitions. These two approaches remain largely disconnected: assessing the adequacy of observed follow-up does not generally quantify the additional follow-up required under an explicit tolerance, whereas estimating time to cure does not itself provide a formal inferential assessment of the available follow-up. We develop a unified parametric framework that combines the Parametric Follow-up Sufficiency Test, which compares the terminal Kaplan-Meier estimate with the cure fraction estimated from a parametric mixture cure model, with two complementary tolerance-based formulations: the Plateau Distance Criterion on the population survival scale and the Residual Survival Criterion on the susceptible survival scale. We derive closed-form expressions for both criteria under the Weibull mixture cure model and establish that they yield identical minimum follow-up times under an appropriate transformation of their tolerance parameters. We evaluate the finite-sample performance of the framework through Monte Carlo simulations across sample sizes, cure fractions, and administrative censoring scenarios. Applications to prostate cancer and triple-negative breast cancer data illustrate how the framework assesses available follow-up, estimates additional observation time under prespecified tolerances, and identifies settings in which follow-up is already adequate.
CommentsR code is available at https://github.com/luizsilvaresende/pfst-cure-models
