Semiparametric inference of survival data

生存数据的半参数推断

• 批准号: 7734846
• 负责人: Rajeshwari Sundaram
• 金额: $ 10.89万
• 依托单位: EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7734846
• 关键词: Address; Clinic; Confidence Intervals; Data; Disease regression; Equation; Event; Experimental Designs; Failure; Fertility; Gynecologist; Human; Measurement; Methods; Modeling; Participant; Performance; Pregnancy; Pregnancy loss; Pregnant Women; Primary biliary cirrhosis; Property; Research Design; Risk; Sampling; Standards of Weights and Measures; Statistical Methods; Statistical Models; Techniques; Time; Visit; Woman; Work; base; follow-up; hazard; malignant stomach neoplasm; simulation; treatment effect

We developed methods to build confidence interval and bands to detect time-varying effects of treatments in Cox's proportional hazards regression model. Dr. Sundaram and her collaborators have addressed in the need for statistical inference which in confidence intervals and confidence bands which give more tighter intervals than the standard Wald-type (normal approximation based) confidence intervals/band. This work extended the results of Tian, Zucker and Wei (2005, JASA) and shows that the proposed intervals/bands are tighter than those proposed by Tian, Zucker and Wei. This was achieved by developing empirical likelihood (EL) point-wise confidence regions for the time-dependent regression coefficients via local partial likelihood smoothing. Asymptotic properties were established for the proposed methods. Extensive numerical studies conducted indicated that the EL point-wise/simultaneous confidence regions/bands have better performance than the Wald-type estimators. The proposed methods illustrated on two real examples: the gastric cancer data and the Mayo Clinic primary biliary cirrhosis data showed similar findings of more precise (narrower) confidence intervals (bands). Another project developed method for randomly truncated data which are frequently encountered when the study design is retrospective and/or due to inability of experimental design to be able to capture the study participant before the initiation of the event. For example, pregnant women get selected based on their first visit to the gynecologist for confirming their pregnancy, resulting in loss to follow up of women who had early pregnancy loss. Estimation based on randomly truncated data becomes very challenging as the risk set over time is non-monotonic, making it very different from random right censoring. One of the problems addressed by Dr. Sundaram is developing robust inference for two sample accelerated failure time data for this type of data. Dr. Sundaram has also developed robust methods for analyzing proportional odds model for randomly truncated data. Proportional odds model provides a useful alternative to proportional hazards when the hazards converge over time (e.g., for modeling treatments that are successful). The large sample properties like asymptotic normality and strong consistency of the proposed estimators were established and the finite sample properties investigated through extensive simulations indicating good performance. The proposed methods are easy to compute, which is not the case with likelihood based estimators as it is not possible to profile out the non-parametric (baseline odds function) as is done with proportional hazards and right censored data.

我们开发了建立置信区间和带的方法，以检测考克斯比例风险回归模型中治疗的时变效应。Sundaram博士和她的合作者已经解决了在置信区间和置信带中的统计推断的需要，这些置信区间和置信带给出比标准Wald型（基于正态近似）置信区间/带更紧的区间。这项工作扩展了Tian，Zucker和Wei（2005，JASA）的结果，并表明所提出的间隔/带比Tian，Zucker和Wei提出的更紧。 这是通过开发经验似然（EL）逐点置信区域的时间依赖性回归系数通过局部偏似然平滑。所提出的方法的渐近性质。 进行了大量的数值研究表明，EL逐点/同时置信区/带有更好的性能比Wald型估计。 所提出的方法说明了两个真实的例子：胃癌数据和马约诊所原发性胆汁性肝硬化数据显示出更精确（窄）的置信区间（带）的类似结果。 另一个项目开发了随机截断数据的方法，当研究设计是回顾性的和/或由于实验设计无法在事件开始前捕获研究参与者时，经常会遇到这种情况。例如，孕妇是根据她们第一次去妇科医生那里确认怀孕而被选中的，这导致了早期流产的妇女无法随访。基于随机截断数据的估计变得非常具有挑战性，因为随着时间的推移风险集是非单调的，这使得它与随机右删失非常不同。 Sundaram博士解决的问题之一是为这类数据的两个样本加速失效时间数据开发鲁棒性推断。Sundaram博士还开发了用于分析随机截断数据的比例优势模型的稳健方法。当风险随时间收敛时（例如，用于对成功的治疗进行建模）。建立了估计量的渐近正态性和强相合性等大样本性质，并通过大量的模拟研究了有限样本性质，表明了良好的性能。所提出的方法易于计算，而基于似然估计的情况并非如此，因为不可能像比例风险和右删失数据那样描绘出非参数（基线优势函数）。