Collaborative Research: Extension of Quantile Regression and Empirical Likelihood Analysis for Censored Data

合作研究：分位数回归的扩展和截尾数据的经验似然分析

• 批准号: 1007666
• 负责人: Mai Zhou
• 金额: $ 7.29万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1007666&HistoricalAwards=false
• 关键词: Collaborative; Research; Extension; Quantile; Regression

Linear models analysis is one of the most appealing statistical methods for its directly interpretable results. The accelerated failure time (AFT) and censored quantile regression (QR) model serve counterparts of the classical linear and uncensored QR model for censored data, and complement the Cox-proportional hazards model. Censored QR, in particular, enriches linear models analysis for censored data by allowing non-constant covariate effects across the distribution of event times. Other regression methods unduly constrain the covariate effects to be constant and fail to provide consistent results. In contrast censored QR allows the treatment effect to be negative for more severe cases (with shorter event-free survival times) but positive in other cases. The AFT and censored QR model are, however, under-utilized as flexible and general methods for estimation, variable selection and inference do not exist. This investigation includes developing (A) flexible estimation methods that work under less stringent conditions than those for existing methods, (B) methods for variable selection, including high dimensional data, and (C) general empirical likelihood(EL) methods parallel to uncensored case. In addition, the general ideas of the proposed research and method developed are applicable to truncation or other censoring types, although they are developed under random right censoring mechanism.Improving statistical models for predicting medical outcomes is always an important part of statistical research. Thanks to recent advancement in high throughput technologies, a vast amount of potentially useful information, including patient's gene profile, is available and anticipated to lead to much improved prediction. The proposed study investigates novel methods to incorporate those data in building a better statistical model to more accurately predict a patient survival. The type of models to be investigated are also more sophisticated: instead of predicting only an "average" person's survival, they allow prediction for "top 10%, or "bottom 10%", while allowing the survivals can be very differently impacted by the gene profile.

线性模型分析因其结果直观而成为最具吸引力的统计方法之一。加速失效时间（AFT）和截尾分位数回归（QR）模型是经典线性和未截尾QR模型的对应模型，是Cox比例风险模型的补充。特别是删失QR，通过允许事件时间分布中的非恒定协变量效应，丰富了删失数据的线性模型分析。其他回归方法过度地将协变量效应限制为常数，无法提供一致的结果。相比之下，删失QR允许治疗效果对于更严重的病例为阴性（无事件生存时间较短），但在其他病例中为阳性。然而，AFT和截尾QR模型未得到充分利用，因为不存在用于估计、变量选择和推断的灵活和通用方法。这项研究包括开发（A）灵活的估计方法，工作在较不严格的条件下比现有的方法，（B）的变量选择的方法，包括高维数据，和（C）一般的经验似然（EL）方法平行于未删失的情况。此外，本文所提出的研究思路和方法虽然是在随机右删失机制下发展起来的，但也适用于截断或其他删失类型。改进预测医疗结果的统计模型一直是统计研究的重要组成部分。由于高通量技术的最新进展，大量的潜在有用的信息，包括患者的基因谱，是可用的，并预计导致大大改善预测。这项研究调查了新的方法，将这些数据纳入建立更好的统计模型，以更准确地预测患者的生存率。要研究的模型类型也更复杂：它们不仅可以预测“平均”人的生存率，还可以预测“前10%”或“后10%”，同时允许基因图谱对生存率的影响非常不同。