Time-Variant Effects of Cancer Risk Factors in Nested Case-Control Studies

描述（由申请人提供）：在许多癌症流行病学队列研究中，感兴趣的疾病往往是罕见的，但研究假设是复杂的，危险因素很多。这些研究通常需要长期随访，以获得足够数量的癌症事件，并阐明疾病的进程。因此，收集整个队列的数据可能会非常昂贵。嵌套病例对照（NCC）设计是一种流行的抽样方法，主要是由于其成本效益。实践中，NCC数据通常使用Cox比例风险（PH）模型进行分析。PH模型的一个直接结果是，假设具有不同协变量值的风险函数的比例在整个随访期间保持不变。由于长期观察的性质和在大规模癌症研究中有待探索的关系的复杂性，比例风险假设很容易被违反。因此，扩展Cox模型以适应时变协变量效应不仅是提高建模灵活性的必要条件，也是阐明癌症病因的关键。然而，这种灵活模型的方法发展主要集中在队列研究上，它们在NCC研究中的应用仍然有限。在本项目中，我们建议研究具有时变系数的Cox模型，以表征NCC研究中癌症危险因素的时间效应。在目标1中，我们建议开发统计方法来使用核加权部分似然方法估计时变系数函数；构造时变系数估计的逐点置信区间和同时置信区间；检验和识别特定危险因素是否存在时变效应；研究了NCC数据时变系数Cox模型中的变量选择问题。一旦提出的方法的渐近性质在理论上建立起来，并且推理过程通过广泛的蒙特卡罗模拟研究得到验证，我们就可以实施我们提出的方法来追求目标2，这将侧重于真实数据分析和软件开发。目标2的第一部分将通过与纽约大学妇女健康研究（NYUWHS）的合作来完成。然后开发和贡献一个开源R包将使所提出的方法免费提供给实际研究人员。这些研究的成功完成将为阐明NCC研究中危险因素对癌症发展的时间影响提供一系列先进的统计推断方法，这也将大大提高我们在NCC数据分析中的建模灵活性，并可以评估和验证从其他方法获得的结果。此外，在纽约大学whs的应用将为我们对潜在危险因素在癌症病因学中的作用提供新的见解和更好的理解。开发免费软件包的主要精神贡献在于，它将把先进的统计方法转化为实际有用和易于使用的工具。

DESCRIPTION (provided by applicant): In many cancer epidemiology cohort studies, the disease of interest is often rare but the study hypothesis is complex with a large number of risk factors. These studies usually require a long-term follow-up to obtain an adequate number of cancer events and to elucidate the course of the disease. Therefore, it can be prohibitively expensive to assemble data for the entire cohort. Nested case-control (NCC) design is a popular sampling method prominently due to its cost-effectiveness. In practice, NCC data are commonly analyzed using Cox's proportional hazards (PH) model. A direct consequence of the PH model is that the ratio of hazard functions with different covariate values is assumed to remain constant over the entire follow-up period. Due to the nature of long-term observation and complexity of the relationship to be explored in large-scale cancer studies, the proportional hazards assumption may easily be violated. Therefore, extensions of Cox's model to accommodate time-varying covariate effects not only are necessary to improve the modeling 0exibility but also are critical to elucidate the etiology of cancer. However, methodology developments for such 0exible models have been mainly focused on cohort studies and their uses in NCC studies remain limited. In this project, we propose to study the Cox model with time-varying coe1cients to characterize temporal effects of cancer risk factors in NCC studies. In Aim 1, we propose to develop statistical methodologies to estimate the time-varying coe1cient functions using a kernel-weighted partial likelihood approach; to construct point-wise and simultaneous confidence intervals of the estimated time-varying coe1cients; to test and identify the existence of time-varying effect of specific risk factor; and to investigate the variable selection problem in the Cox model with time-varying coe1cients for NCC data. Once the asymptotic properties of the proposed method are established in theory and the inference procedures are validated using extensive Monte Carlo simulation studies, we can implement our proposed approaches to pursue Aim 2, which will focus on real data analyses and software development. The first part of Aim 2 will be accomplished through collaborations with the New York University Women Health Study (NYUWHS). Then developing and contributing an open-source R package will make the proposed methodologies freely available to practical researchers. Successful completion of the proposed studies will provide a series of advanced statistical inference approaches to elucidating the temporal effects of risk factors on the cancer development for NCC studies, which will also substantially improve our modeling 0exibility in the analysis of NCC data, and can assess and validate the results obtained from other methods. Furthermore, the application in NYUWHS will provide us new insights and better understanding on the effects of potential risk factors in cancer etiology. The fund mental contribution of development of freely-available software package is that it will translate the advanced statistical methodologies into practically useful and accessible tools. PUBLIC HEALTH RELEVANCE: PROJECT NARRATIVE: Nested case-control design, a cost-effective sampling method commonly used in cancer epidemiologic studies, necessitates developing 0exible statistical approaches to evaluate the association between cancer and risk factors. This research project proposes to develop statistical models and inference approaches to accommodating and characterizing temporal effects of cancer risk factors for NCC studies, to provide new aspects and novel insights into the temporal relation between disease and its risk factors, and to elucidate our understanding of cancer etiology. Furthermore, contributing freely available software is essential to equip practical investigators with alternative tools to analyze NCC data and to assess, compare and validate study results.