Flexible Statistical Methods for Complex Survival Data in Biomedical Studies

描述（由申请人提供）：本研究的广泛、长期目标是开发新的统计方法，用于分析流行病学研究和临床试验的生存数据。在生存数据分析中的统计建模和推理方面取得了重大进展；然而，由于新的研究设计、先进的技术以及医学研究的规模和复杂性不断扩大，仍然存在许多悬而未决的问题和新出现的挑战。在本研究中，我们将探讨两类一般的半参数模型，即转换模型和加速失效时间模型，用于分析复杂的生存数据。这些模型不仅是对Cox比例风险模型的补充，而且为生存数据建模提供了一般的回归框架和更好的策略。因此，它们通过提供全面的生存分析在许多生物医学应用中发挥重要作用。我们寻求发展统计上合理的方法，不仅正确使用数据信息和结构，而且功能强大，计算效率高。由于研究者在纽约大学妇女健康研究（NYUWHS）和砷对健康的影响纵向研究（HEALS）的合作工作中出现的问题，我们的方法发展包括以下四个具体目标：(1)探讨嵌套病例对照（NCC）研究中的一类广义线性转换模型；（2.) 通过统一的基于似然的方法，研究病例队列（CC）和嵌套病例对照研究中加速失效时间（AFT）模型的有效估计；(3)。开发AFT治愈模型的半参数贝叶斯推理方法，用于分析来自易感和非易感（治愈）受试者混合人群的队列研究或临床试验的生存数据；(4)。研究来自队列研究或临床试验的部分线性回归模型和相关的推断程序。拟议项目的结果将与许多生物医学研究相关并适用。在所有的具体目标中，我们将研究所提出的估计器的理论性质，并开发可靠的数值算法来实现所提出的估计方法。还将特别努力为从业人员开发和传播软件。我们将进行广泛的模拟研究，以评估理论的相关性和提出的估计器的有限样本性能。我们还将调查所提出的方法在已发表的数据集上的性能，将它们与现有方法进行比较，并展示它们在主要临床和流行病学研究中的应用，包括纽约大学卫生和健康研究。1

DESCRIPTION (provided by applicant): The broad, long-term objectives of this research are the developments of new statistical methodology for the analysis of survival data from both epidemiological studies and clinical trials. Significant progress has been made in statistical modeling and inference in survival data analysis; however, there are still many open questions and emerging challenges posed by new study designs, advanced technologies, as well as the growing scale and complexity of medical studies. In this proposed research, we will explore two general classes of semiparametric models, the transformation model and the accelerated failure time model, for analyzing complex survival data. These models not only are complements to Cox's proportional hazards model, but also provide general regression frameworks and possibly better strategies for modeling survival data. Thus, they play important roles in many biomedical applications by offering comprehensive survival analysis. We seek to develop statistically sound methods that not only make proper use of data information and structure but also are powerful and computationally efficient. Motivated by problems arising from the investigators' collaborative work on the New York University Women's Health Study (NYUWHS) and the Health Effects of Arsenic Longitudinal Study (HEALS), our methodology developments include the following four specific aims: (1.) To explore a broad class of linear transformation models in nested case-control (NCC) studies; (2.) To investigate efficient estimation of the accelerated failure time (AFT) model in case-cohort (CC) and nested case-control studies through a unified likelihood-based approach; (3.) To develop semiparametric Bayesian inference methods for the AFT cure model for the analysis of survival data from cohort studies or clinical trials in an admixture population with susceptible and non-susceptible (cured) subjects; (4.) To study partially linear regression modeling and the associated inference procedures for censored survival data from cohort studies or clinical trials. Results from the proposed project will be relevant and applicable to many biomedical studies. In all the specific aims, we will study the theoretical properties of the proposed estimators, and develop reliable numerical algorithms for implementing the proposed estimation methods. Special effort will also be devoted to developing and disseminating software for practitioners. We will carry out extensive simulation studies to evaluate relevance of the theory and the finite sample performance of the proposed estimators. We will also investigate the performance of the proposed methods on published datasets, compare them with existing approaches and demonstrate their applications in major clinical and epidemiological studies, including the NYUWHS and the HEALS. 1