Department of Epidemiology and Biostatistics, Memorial Sloan Kettering, 485 Lexington Avenue, New York, New York, 10017, USA.
Lifetime Data Anal. 2021 Jan;27(1):1-14. doi: 10.1007/s10985-020-09509-x. Epub 2020 Oct 22.
Calibration is an important measure of the predictive accuracy for a prognostic risk model. A widely used measure of calibration when the outcome is survival time is the expected Brier score. In this paper, methodology is developed to accurately estimate the difference in expected Brier scores derived from nested survival models and to compute an accompanying variance estimate of this difference. The methodology is applicable to time invariant and time-varying coefficient Cox survival models. The nested survival model approach is often applied to the scenario where the full model consists of conventional and new covariates and the subset model contains the conventional covariates alone. A complicating factor in the methodologic development is that the Cox model specification cannot, in general, be simultaneously satisfied for nested models. The problem has been resolved by projecting the properly specified full survival model onto the lower dimensional space of conventional markers alone. Simulations are performed to examine the method's finite sample properties and a prostate cancer data set is used to illustrate its application.
校准是预测风险模型准确性的重要措施。当结果是生存时间时，校准的一个广泛使用的度量标准是预期的 Brier 得分。在本文中，开发了一种方法来准确估计来自嵌套生存模型的预期 Brier 得分的差异，并计算此差异的伴随方差估计。该方法适用于时间不变和时变系数 Cox 生存模型。嵌套生存模型方法通常应用于以下情况：完整模型由常规和新协变量组成，子集模型仅包含常规协变量。方法学开发中的一个复杂因素是，Cox 模型规范通常不能同时满足嵌套模型的要求。通过将适当指定的完整生存模型投影到仅常规标记的较低维空间来解决该问题。进行了模拟以检查该方法的有限样本特性，并使用前列腺癌数据集来说明其应用。
