Joint Modeling of Longitudinal Physical Activity and Diet Data and Survival

项目总结/摘要 本项目是对PAR-18 - 857《饮食和体力活动评估方法》的回应。 我们主要对开发和应用创新的统计方法进行联合建模感兴趣， 发病率或死亡率结果以及纵向体力活动和饮食摄入量数据。身体活动 和饮食是参与许多慢性疾病病因学的重要行为因素，如心血管疾病， 血管疾病和癌症。我们的动机是纵向的身体活动测量问题所涉及的 妇女健康倡议（WHI）和健康，饮食，活动和生活方式（HEAL）研究。WHI 一项观察性研究（OS）是对93，676名绝经后妇女的健康结局进行的前瞻性研究 1993年至1998年在美国40个临床中心登记。HEAL研究招募了大约1，200名 1997 - 1998年期间确诊的早期乳腺癌妇女， 超过15年。许多营养或体力活动指标可能具有零值（或低值）， 价值（tectable value）在一群人中。我们的初步模拟结果表明， 不考虑测量误差的简单方法，或用于mea的标准偏差校正， 不考虑具有零值的个体子集的测量误差可能导致偏差 回归分析中的效应估计。本提案的具体目标包括：（一）制定和 在二元发病率或死亡率结果的联合建模中应用方法来调整测量误差 和纵向措施的身体活动和饮食摄入量的数据，这可能是零之间的一些指数， 活力四射。(ii)开发和应用调整时间-事件联合建模中测量误差的方法 生存结果和身体活动和饮食摄入数据的纵向测量，这可能是零 在一些个体中。(iii)开发和应用方法，用于时间到时间之间的时变关联， 事件结果和身体活动和饮食摄入数据的纵向测量。所建议模型 和方法将被应用于从WHI-OS和HEAL的身体活动和饮食数据 study.我们的新方法将通过R语言编写成用户友好的子程序，可供统计学家使用， 流行病学家和其他公共卫生专业人员。提案中提出的方法将具有 一般应用于其他研究，其中纵向身体活动和饮食数据可用。

Project Summary/Abstract The proposed project is in response to PAR-18-857, Diet and Physical Activity Assessment Methodology. We are primarily interested in developing and applying innovative statistical methods for joint modeling of incidence or mortality outcomes and longitudinal physical activity and dietary intake data. Physical activity and diet are important behavioral factors involved in the etiology of many chronic diseases, such as cardio- vascular disease and cancer. We are motivated by longitudinal physical activity measurement issues involved in the Women's Health Initiative (WHI) and the Health, Eating, Activity, and Lifestyle (HEAL) study. The WHI observational study (OS) is a prospective study of health outcomes among 93,676 postmenopausal women enrolled between 1993 and 1998 in 40 U.S. clinical centers. The HEAL study enrolled approximately 1,200 women with early stage breast cancer, diagnosed between 1997-1998, and who have been followed up for more than 15 years. Many nutrient or physical activity measures may have a zero value (or a low de- tectable value) among a group of individuals. Our preliminary simulation results demonstrate that a naive method without taking into account measurement error, or a standard bias correction for mea- surement error without taking into account a subset of individuals with zero values may lead to bias in the effect estimation in regression analysis. Specific aims of this proposal include: (i) To develop and apply methods to adjust for measurement error in joint modeling of binary incidence or mortality outcomes and longitudinal measures of physical activity and dietary intake data, which may be zero among some indi- viduals. (ii) To develop and apply methods to adjust for measurement error in joint modeling of time-to-event survival outcomes and longitudinal measures of physical activity and dietary intake data, which may be zero among some individuals. (iii) To develop and apply methods for time-varying association between time-to- event outcomes and longitudinal measures of physical activity and dietary intake data. The proposed models and methods will be applied to the physical activity and dietary data from both the WHI-OS and the HEAL study. Our new methods will be written in user friendly subroutines via R, which can be used by statisticians, epidemiologists, and other public health professionals. The methods developed in the proposal will have general applications to other studies where longitudinal physical activity and dietary data are available.