Joint Modeling of Longitudinal Physical Activity and Diet Data and Survival

项目摘要/摘要 拟议的项目是对PAR-18-857、饮食和体力活动评估方法的响应。 我们主要感兴趣的是开发和应用创新的统计方法，用于联合建模 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。体力活动 和饮食是许多慢性疾病的重要行为因素，如心血管疾病。 血管疾病和癌症。我们的动机是涉及纵向体力活动测量问题 在妇女健康倡议(WHI)和健康、饮食、活动和生活方式(Hear)研究中。WHI 观察性研究(OS)是对93,676名绝经后妇女健康状况的前瞻性研究 1993至1998年间在40个美国临床中心注册。Hear研究招募了大约1200人 1997-1998年间被诊断为早期乳腺癌的妇女，并对其进行了随访 超过15年了。许多营养或体力活动指标可能为零值(或低DE值)。 可教化的价值)。我们的初步模拟结果表明， 不考虑测量误差的朴素方法，或对测量误差进行标准偏差校正。 没有考虑零值个体子集的确定误差可能会导致偏差 在回归分析中的效果估计。这项建议的具体目标包括：(I)开发和 在二元发病率或死亡率结果的联合建模中应用方法调整测量误差 以及体力活动和饮食摄入数据的纵向测量，在某些指标中可能为零- 活人。(2)开发和应用调整事件间隔时间联合建模中的测量误差的方法 生存结果和体力活动的纵向测量以及饮食摄入量数据，可能为零 在一些个人中。(3)开发和应用时间与时间之间的时变关联的方法 活动结果以及体力活动和饮食摄入量数据的纵向测量。建议的模型 方法将应用于来自WHI-OS和Hear的体力活动和饮食数据 学习。我们的新方法将通过R以用户友好的子例程编写，统计学家可以使用R， 流行病学家和其他公共卫生专业人士。建议书中开发的方法将具有 一般应用于其他可获得纵向体力活动和饮食数据的研究。

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.