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

巢式病例对照研究中癌症危险因素的时变效应

• 批准号: 7991942
• 负责人: Mengling Liu
• 金额: $ 9.44万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7991942
• 关键词: Cancer Etiology; Case-Control Studies; Cohort Studies; Collaborations; Complex; Computer software; Confidence Intervals; Cox Models; Cox Proportional Hazards Models; Data; Data Analyses; Development; Disease; Epidemiologic Studies; Event; Funding; Knowledge; Malignant Neoplasms; Methodology; Methods; Modeling; Nature; Nested Case-Control Study; New York; Procedures; Property; Proportional Hazards Models; Psyche structure; Research; Research Personnel; Research Project Grants; Risk Factors; Sampling; Series; Statistical Models; Testing; Time; Translating; Universities; Variant; Weight; Women' s Health; cancer epidemiology; cancer risk; case control; cohort; cost; cost effectiveness; design; epidemiology study; follow-up; hazard; improved; insight; interest; novel; open source; public health relevance; simulation; software development; theories; tool

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.

描述（由申请人提供）：在许多癌症流行病学队列研究中，感兴趣的疾病通常很罕见，但研究假设很复杂，存在大量危险因素。这些研究通常需要长期随访才能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。巢式病例对照（NCC）设计因其成本效益而成为一种流行的抽样方法。在实践中，NCC 数据通常使用 Cox 比例风险 (PH) 模型进行分析。 PH 模型的直接结果是假设具有不同协变量值的危险函数的比率在整个随访期间保持恒定。由于长期观察的性质以及大规模癌症研究中要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对于提高建模灵活性是必要的，而且对于阐明癌症的病因也至关重要。然而，此类 0exible 模型的方法开发主要集中在队列研究上，其在 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影响。在目标 1 中，我们建议开发统计方法，使用核加权偏似然方法来估计时变系数函数；构建估计的时变系数的逐点和同时置信区间；测试并识别特定风险因素的时变效应是否存在；并研究NCC数据时变系数Cox模型中的变量选择问题。一旦所提出方法的渐近特性在理论上得到确立，并且使用广泛的蒙特卡罗模拟研究验证了推理程序，我们就可以实施我们提出的方法来实现目标 2，该目标将侧重于实际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女性健康研究 (NYUWHS) 的合作来完成。然后开发并贡献一个开源 R 包将使实际研究人员可以免费使用所提出的方法。拟议研究的成功完成将为NCC研究提供一系列先进的统计推断方法来阐明危险因素对癌症发展的时间影响，这也将大大提高我们在NCC数据分析中的建模0灵活性，并可以评估和验证其他方法获得的结果。此外，纽约大学WHS 的应用将为我们提供新的见解并更好地理解潜在风险因素对癌症病因学的影响。开发免费软件包的根本贡献在于它将先进的统计方法转化为实用且易于使用的工具。 公共卫生相关性：项目叙述：巢式病例对照设计是癌症流行病学研究中常用的一种具有成本效益的抽样方法，需要开发灵活的统计方法来评估癌症与危险因素之间的关联。该研究项目旨在开发统计模型和推理方法，以适应和表征 NCC 研究中癌症风险因素的时间影响，为疾病与其风险因素之间的时间关系提供新的方面和新颖的见解，并阐明我们对癌症的理解病因学。此外，免费提供软件对于为实际研究人员配备分析 NCC 数据以及评估、比较和验证研究结果的替代工具至关重要。