Zhang Yilong, Shao Yongzhao
Merck Research Laboratories, 126 E. Lincoln Ave., Rahway, NJ 07065, USA.
Department of Population Health, New York University School of Medicine, 650 first ave 5th FL, New York, NY 10016, USA
Biostatistics. 2018 Jan 1;19(1):14-26. doi: 10.1093/biostatistics/kxx016.
Many populations of early-stage cancer patients have non-negligible latent cure fractions that can be modeled using transformation cure models. However, there is a lack of statistical metrics to evaluate prognostic utility of biomarkers in this context due to the challenges associated with unknown cure status and heavy censorship. In this article, we develop general concordance measures as evaluation metrics for the discriminatory accuracy of transformation cure models including the so-called promotion time cure models and mixture cure models. We introduce explicit formulas for the consistent estimates of the concordance measures, and show that their asymptotically normal distributions do not depend on the unknown censoring distribution. The estimates work for both parametric and semiparametric transformation models as well as transformation cure models. Numerical feasibility of the estimates and their robustness to the censoring distributions are illustrated via simulation studies and demonstrated using a melanoma data set.
许多早期癌症患者群体具有不可忽视的潜在治愈比例，可使用变换治愈模型进行建模。然而，由于存在未知治愈状态和严重删失的挑战，在这种情况下缺乏评估生物标志物预后效用的统计指标。在本文中，我们开发了通用一致性度量，作为变换治愈模型（包括所谓的促进时间治愈模型和混合治愈模型）判别准确性的评估指标。我们给出了一致性度量一致估计的显式公式，并表明它们的渐近正态分布不依赖于未知的删失分布。这些估计适用于参数和半参数变换模型以及变换治愈模型。通过模拟研究说明了估计的数值可行性及其对删失分布的稳健性，并使用黑色素瘤数据集进行了验证。
