Motivated by risk prediction studies with ultra-high dimensional bio markers, we propose a novel improvement screening methodology. Accurate risk prediction can be quite useful for patient treatment selection, prevention strategy or disease management in evidence-based medicine. The question of how to choose new markers in addition to the conventional ones is especially important. In the past decade, a number of new measures for quantifying the added value from the new markers were proposed, among which the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) stand out. Meanwhile, C-statistics are routinely used to quantify the capacity of the estimated risk score in discriminating among subjects with different event times. In this paper, we will examine these improvement statistics as well as the norm-based approach for evaluating the incremental values of new markers and compare these four measures by analyzing ultra-high dimensional censored survival data. In particular, we consider Cox proportional hazards models with varying coefficients. All measures perform very well in simulations and we illustrate our methods in an application to a lung cancer study.

中文翻译：

---

具有审查的生存结果和变化系数的超高维数据的改进筛选。

基于超高维生物标记物的风险预测研究，我们提出了一种新颖的改进筛选方法。准确的风险预测对于循证医学中的患者治疗选择，预防策略或疾病管理非常有用。除传统标记外，如何选择新标记的问题尤为重要。在过去的十年中，提出了许多从新标记中量化增加值的新措施，其中，综合歧视改进（IDI）和净重分类改进（NRI）脱颖而出。同时，通常使用C统计量来量化估计的风险评分在区分具有不同事件时间的受试者时的能力。在本文中，我们将检查这些改进统计数据以及基于准则的方法来评估新标记的增量值，并通过分析超高维删失的生存数据来比较这四种方法。特别地，我们考虑具有不同系数的Cox比例风险模型。所有测量在模拟中均表现良好，我们将其方法应用于肺癌研究。