Evaluating Prediction Models for Cancer Endpoints Subject to Dependent Censoring

描述（由申请人提供）：项目摘要/摘要拟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的预测模型的性能时，感兴趣的终点，如患者生存或癌症复发的时间受到潜在的依赖删失，这是经常出现在观察和流行病学研究。癌症风险和预后预测模型的重要性已经得到很好的确立：它们可用于识别高风险个体，规划干预性试验，随后设计和改进个性化预防和治疗策略，并估计人口负担，癌症成本以及潜在干预和治疗的影响。为了识别最佳（或更好）预测模型， 至关重要的是，要制定可靠的预测准确性指标，以评估和比较预测模型。不针对删失机制进行调整的预测准确性指标可能会导致在存在相关删失的情况下对预测模型进行偏倚评估。虽然大量的工作已经报告的预测准确性指标的发展，只有有限的工作预测准确性指标的删失数据，其中大部分已开发的情况下，独立删失和限制考克斯比例风险模型。此外，由于技术的重大进步，在癌症研究中收集基因组和蛋白质组数据等高维生物标志物变得越来越普遍，并且已经开发了现代统计方法来在构建预测模型时利用这些高维数据，这对在依赖删失的情况下评估预测准确性提出了另一个挑战。这些考虑导致我们的具体目标如下：1）在评估癌症终点回归模型的预测准确性时，开发新的指标来解释删失机制; 2）在评估癌症终点加速失效时间模型的预测准确性时，开发新的指标来解释删失机制。3）针对删失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的数据分析对删失数据的预测准确性度量进行系统评估。拟议的统计方法，一旦开发，将允许评估预测模型的预测精度在广泛的设置，包括不同的审查机制和高维数据。建议的数值研究将揭示重要的洞察力的适用性，优点和缺点的不同指标，以及审查机制对这些指标的影响，并随后提供更好的指导癌症研究人员如何使用和解释这些指标在研究和实践中。

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.