Flexible Statistical Methods for Complex Survival Data in Biomedical Studies

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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 PUBLIC HEALTH RELEVANCE: The proposed research aims to develop novel statistical approaches for analyzing survival data under various study designs, from admixed populations, and with complex covariates effects. The completion of our proposed research will provide reliable and efficient statistical methods for complex survival data that are commonly encountered in clinical and epidemiological studies. These methods can facilitate scientists' understanding of etiology of complex diseases and eventually lead to better design of disease prevention, prognosis and treatment strategies to improve human health. 1