Evaluating Prediction Models for Cancer Endpoints Subject to Dependent Censoring

描述(由申请人提供)：项目摘要/摘要建议的研究旨在开发新的统计方法，用于评估癌症风险和预后预测模型的性能，当关注的终点，如患者存活或癌症复发时间受到潜在的依赖审查时，这通常出现在观察性和流行病学研究中。癌症风险和预后预测模型的重要性已经得到了很好的确立：它们可以用来识别高危个体，计划介入试验，随后设计和改进个性化的预防和治疗策略，并估计人口负担、癌症成本以及潜在干预和治疗的影响。为了识别最优(或更好)预测模型， 对于评估和比较预测模型，制定稳健的预测精度度量至关重要。没有针对审查机制进行调整的预测精度度量可能导致在存在依赖审查的情况下对预测模型的有偏见的评估。虽然已经报道了大量关于发展预测精度度量的工作，但关于删失数据的预测精度度量的工作有限，其中大多数都是针对独立审查的情况而开发的，并且仅限于Cox比例风险模型。此外，由于技术的重大进步，在癌症研究中收集高维生物标记物，如基因组和蛋白质数据已经变得越来越普遍，现代统计学方法已经发展起来，在构建预测模型时利用这些高维数据，这为在存在依赖审查的情况下评估预测准确性提出了另一个挑战。这些考虑导致了我们的具体目标如下：1)开发新的指标来说明审查机制，以评估接受相关审查的癌症终点回归模型的预测准确性；2)开发新的指标来说明审查机制，当评估加速失效时间模型的预测准确性时，考虑到加速失效时间模型；3)针对审查可能取决于未观察到的生存时间的情况进行敏感性分析；以及4)通过广泛的模拟和真实数据分析，对被审查数据的预测准确性指标进行系统评估。一旦制定了拟议的统计方法，就可以在包括不同审查机制和高维数据在内的各种情况下评估预测模型的预测准确性。所提出的数值研究将对不同指标的适用性、优缺点以及审查机制对这些指标的影响提供重要的见解，从而为癌症研究人员在研究和实践中如何使用和解释这些指标提供更好的指导。

DESCRIPTION (provided by applicant): Project Summary/Abstract The proposed research seeks to develop new statistical methods for assessing performance of prediction models for cancer risk and prognosis when the endpoint of interest such as patient survival or time to cancer recurrence is subject to potentially dependent censoring, which is often present in observational and epidemiological studies. The significance of prediction models for cancer risk and prognosis has been well established: they can be used to identify individuals at high risk, plan interventional trials and subsequently design and improve personalized prevention and treatment strategies, and estimate the population burden, the cost of cancer, and the impact of potential interventions and treatments. In order to identify optimal (or better) prediction models, it is crucial to develop robust predictive accuracy metrics for assessing and comparing prediction models. Predictive accuracy metrics that do not adjust for censoring mechanism likely lead to biased assessment of prediction models in the presence of dependent censoring. While a considerable amount of work has been reported on development of predictive accuracy metrics, there has been only limited work on predictive accuracy metrics for censored data, most of which have been developed for the case of independent censoring and limited to Cox proportional hazard models. In addition, owing to major advances in technology, it has become increasingly common that high-dimensional biomarkers such as genomic and proteomic data are collected in cancer research studies and modern statistical methods have been developed to utilize these high-dimensional data when constructing prediction models, which presents another challenge for assessing predictive accuracy in the presence of dependent censoring. These considerations lead to our specific aims as follows: 1) develop new metrics to account for censoring mechanism when assessing predictive accuracy of regression models for cancer endpoints that are subject to dependent censoring; 2) develop new metrics to account for censoring mechanism when assessing predictive accuracy of accelerated failure time models for cancer endpoints that are subject to dependent censoring; 3) develop sensitivity analysis for the case where censoring may depend on unobserved survival times; and 4) perform systematic evaluation of predictive accuracy metrics for censored data through extensive simulations and real data analysis. The proposed statistical methods, once developed, will allow for assessment of predictive accuracy of prediction models under a wide range of settings including different censoring mechanisms and for high-dimensional data. The proposed numerical studies will shed important insight on applicability, advantages, and disadvantages of different metrics, as well as impact of censoring mechanism on these metrics, and subsequently provide better guidance to cancer researchers on how to use and interpret these metrics in research studies and in practice.