Method Development for Survival Dynamic Regression in Chronic Disease Research

描述（由申请人提供）：了解危险因素与慢性疾病之间的关系对于改善治疗和患者护理至关重要。基于人口的慢性病数据登记通常是可用的，并为实现这一目的提供了良好的平台。传统回归模型的一个关键方面是假设每个风险因素的影响是恒定的。然而，越来越多的证据表明，许多慢性病的病因是复杂的，风险因素对疾病结局的影响可能不是恒定的。通过我们正在进行的囊性纤维化基金会患者登记（CFFPR）的合作工作，该资助的研究人员已经证明了变系数回归，称为生存分析中的动态回归（Martinussen和Scheike， 2006），为发现风险因素与CF结果（主要CF事件发生的时间和频率）之间关联的重要变化提供了强大的工具。本研究项目的动机是由于CFFPR产生的几个重要的未解决的开放性问题：(i)竞争风险，双重审查和对CF事件的观察的左截断，继承了CFFPR的设计；（ii）高维协变量，这需要开发适应不同协变量效应的变量选择程序；（iii）大样本量，这需要高效的计算；（四）需要利用随时间变化的后续信息来协助疾病预后和解决实质性科学问题。当前的动态回归方法有几个局限性：无法适应数据的复杂特征，解释和预测困难，计算问题。此外，在生存变系数模型下，对变量选择的研究非常有限。这项建议的总目标是发展一个全面的动态回归框架，解决现有方法的主要限制，并有能力处理许多与实际数据有关的问题。为了实现这一目标，我们将首先通过引入合理的建模和开发解释常见生存数据特征的推理程序，构建一个统一的生存动态回归框架（目标1）。我们将解决具有挑战性的高维动态回归问题（目标2），其中假设恒定效果的现有方法可能具有较差的性能。我们将提出一种开创性的动态回归策略，用于研究时间相关协变量与生存结果之间的关系，并允许合理的解释和预测（目标3）。建议的统计方法将应用于cfpr（目标4），并将开发用户友好的软件并提供给一般研究界（目标5）。这项拨款提出的方法发展将对科学调查产生广泛影响，不仅对cfpr，而且对其他基于登记的慢性病研究。

DESCRIPTION (provided by applicant): Understanding the association between risk factors and chronic diseases is crucially important for improvement in treatment and patient care. Population based data registries for chronic disease are often available and provide excellent platforms to serve this purpose. A crucial aspect of traditional regression modeling is the assumption that the effect of each risk factor is constant. However, there is growing evidence that the etiology of many chronic diseases is complex and the influence of a risk factor on disease outcomes may not be constant. Through our ongoing collaborative work on Cystic Fibrosis Foundation patient registry (CFFPR), the investigators of this grant have demonstrated that varying coefficient regression, termed as dynamic regression in survival analysis (Martinussen and Scheike, 2006), provides a powerful tool for discovering important changes in associations between risk factors and CF outcomes (time to, and frequency of, major CF events). This research project is motivated by several important unsolved, open questions arising from CFFPR: (i) competing risk, double censoring and left truncation to the observation of CF events, inherited with the design of CFFPR; (ii) the high dimensional covariates, which necessitate the development of variable selection procedures that accommodate varying covariate effects, (iii) the large sample size, which demands efficient computation; (iv) the need of utilizing time-dependent follow-up information to aid in disease prognosis and address substantive scientific questions. Current dynamic regression approaches have several limitations: inability to accommodate complex features of data, difficulties in interpretation and prediction, computational issues. Moreover, there is very limited work on variable selection under survival varying coefficient models. The overall objective of this proposal is to develop a comprehensive dynamic regression framework that resolves the key limitations of the existing approaches and possesses the capacity to handle many realistic data-related issues. To accomplish this goal, we will first lay out a unified framework of survival dynamic regression by introducing sensible modeling and developing inferential procedures that account for common survival data features (Aim 1). We will tackle the challenging problem of high dimensional dynamic regression (Aim 2), where the existing methods that assume constant effects can have poor performance. We will propose a seminal dynamic regression strategy for investigating the relationship between time-dependent covariates and survival outcomes with sensible interpretations and predictions permitted (Aim 3). The proposed statistical methods will be applied to CFFPR (Aim 4) and user-friendly software will be develop and made available to general research communities (Aim 5). Methodological development proposed in this grant will have a broad impact on scientific investigations not only on CFFPR but also on other registry based chronic disease studies. PUBLIC HEALTH RELEVANCE: We propose statistical methods to identify risk factors for chronic disease studies, such as Cystic Fibrosis. These methods will enhance the understanding of the mechanism and prognosis of chronic diseases that will lead to improved disease treatment and patient care.