Statistical Inferences, Computing, and Applications of Semiparametric Accelerated Failure Time Models

半参数加速失效时间模型的统计推断、计算和应用

• 批准号: 1209022
• 负责人: Jun Yan
• 金额: $ 13万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1209022&HistoricalAwards=false
• 关键词: Statistical; Inferences; Computing; Applications; Semiparametric

Accelerated failure time (AFT) models are much less utilized in practice than relative risk models because of difficulty in inference and limited availability in standard software. The investigators develop 1) generalized estimating equations (GEE) for multivariate AFT models with application to adolescent depression, 2) induced smoothing rank-based approach and least squares approach for AFT models with covariates missing by design, 3) regularized estimation for AFT models with high dimensional covariates, and 4) an open source, high-quality, and user-friendly software implementation for inferences with AFT models. The GEE approach is incorporated into an iterative procedure to estimate the regression coefficients in multivariate AFT models, initializing from a consistent and asymptotically normal estimator obtained with induced smoothing. Inferences with covariates missing by design proceed with appropriately constructed selection weights for estimating functions. Regularized estimation is done by minimizing an objective function, where three novel choices of risk functions are combined with a variety of penalty functions, including nonconvex ones such as minimax concave penalty. Software implementation will be made available as R packages.Methodological development on AFT models is far behind that on relative risk models due to computational and inferential challenges. The investigators shorten the gap with a comprehensive collection of methodologies and software implementation for AFT models in practical settings that are frequently encountered in biomedical, epidemiological, and social science studies. The methodologies and software implementation are expected to have an influential impact on the practice of failure time modeling. The open source implementation provides a realistic alternative to the relative risk model for censored data regression. Applications of the methods to ongoing collaborative projects that motivated the proposed research have cross-boundary effects. A bivariate AFT model for the duration of depression and the duration of major stressors offers a novel perspective to gain insight into onset and maintenance of depressive episodes. The project is naturally integrated with education through undergraduate/graduate student thesis advising, graduate level courses, and short courses at conferences in both the statistics community and the psychology community. The publicly available software makes the cutting-edge statistical methodology accessible to those who need them in scientific discoveries.

由于推理困难和标准软件的可用性有限，因此在实践中，加速故障时间（AFT）模型在实践中的使用率要少得多。 The investigators develop 1) generalized estimating equations (GEE) for multivariate AFT models with application to adolescent depression, 2) induced smoothing rank-based approach and least squares approach for AFT models with covariates missing by design, 3) regularized estimation for AFT models with high dimensional covariates, and 4) an open source, high-quality, and user-friendly software implementation for inferences with AFT models. GEE方法纳入了迭代程序中，以估计多元AFT模型中的回归系数，并从一致且渐近的正常估计器初始化，这些估计量通过诱导的平滑度获得。 设计进行的推断，其协变量进行了适当构建的选择权重以估计功能。 正则化估计是通过最小化目标函数来完成的，其中三种新的风险功能选择与多种惩罚函数结合在一起，包括非凸函数，例如minimax凹面惩罚。 软件实施将作为R软件包提供。由于计算和推论挑战，AFT模型的方法开发远远落后于相对风险模型。研究人员通过在生物医学，流行病学和社会科学研究中经常遇到的实用环境中的方法和软件实施的全面收集来缩短差距。预计方法和软件实施将对故障时间建模实践产生影响。 开源实现为审查数据回归的相对风险模型提供了现实的替代方法。 这些方法在促进拟议研究的正在进行的协作项目中的应用具有跨界影响。 在抑郁症的持续时间内，一个双变量的船尾模型和主要压力源的持续时间提供了一种新颖的观点，可以深入了解抑郁发作的发作和维持。该项目通过本科/研究生论文，研究生级课程以及在统计社区和心理学社区的会议上的简短课程与教育自然融入教育。公开可用的软件使在科学发现中需要它们的人可以访问尖端的统计方法。