Novel Statistical Models for Intensive Longitudinal Analyses of Cancer Control Behaviors

项目摘要 本提案是对PAR-18-352（“行为和社会领域的方法和测量”）的回应。 科学”）。它将开发，应用，测试和传播多层次的统计模型和软件， 由时变变量组成的个体内均值、方差、斜率对癌症控制风险的影响 因素和结果。癌症仍然是导致死亡的主要原因。大约42%的新癌症病例， 在美国，19%的疾病是由吸烟引起的，18%的疾病是由肥胖引起的。 体重、缺乏运动、过量饮酒和营养不良。密集纵向数据（ILD） 方法，收集在微观时间尺度上以高密度捕获的许多评估（例如，秒， 分钟，小时）使用实时数据捕获方法（例如，移动的传感和加速度测量），提供 深入了解癌症风险因素和结果的动态性质的巨大机会。在ILD研究中， 每个对象通常有数百到数千个观察结果，这使我们能够建立模型 个体内参数包括时变变量如平均值（例如，一个主题有多不快乐， 平均而言，在不同的情况下？），方差（例如，受试者的情绪在不同场合下有多不稳定？），和斜坡 (e.g.,受试者的情绪是否与跨场合的能量感觉有关？）。在我们的初步工作中， 开始开发一系列两阶段多水平统计模型来测试个体内均值、方差和 斜率参数作为受试者水平结局的预测因子、中介因子和调节因子。我们已经证明 在感觉精力充沛方面，个体内差异较大，但不是平均值，与会议的可能性较低相关。 身体活动指南。我们还发现，积极情感的个体内差异会调节 个体内平均积极情感与抑郁症状和饮酒的关系。 然而，目前的数据分析技术在几个关键领域受到限制，这严重限制了我们的能力， 充分利用ILD的潜力，加强癌症控制方面的行为和社会科学研究。到 为了弥补这些差距，我们提出以下目标。具体来说，我们将开发能够 (1)估计嵌套在时间或时间内的时变变量的个体内方差和斜率的效应 人群，（2）预测嵌套在时间或人群内的癌症控制结果，（3）比较 几个个体内方差的相对预测强度，以及（4）检查个体内方差或 计数或有序变量的斜率。我们将通过进行二次分析来测试这些统计扩展 来自三项使用ILD的癌症对照研究的数据（643人中有94，892例病例），包括 儿童、中年人、亲子二人组。我们还将开发、测试和传播一个独立的 具有GUI的软件，能够运行这些统计模型，供应用行为和社会科学使用 研究人员待开发的方法可以很容易地推广到各种其他疾病领域，如哮喘， 饮食失调、自杀预防、艾滋病毒风险、药物依从性和环境暴露。

PROJECT SUMMARY This proposal responds to PAR-18-352 (“Methodology and Measurement in the Behavioral and Social Sciences”). It will develop, apply, test, and disseminate multilevel statistical models and software for estimating effects of intraindividual means, variances, slopes comprised of time-varying variables on cancer control risk factors and outcomes. Cancer remains a leading cause of mortality. Approximately 42% of new cancer cases in the U.S. are viewed as potentially avoidable including 19% caused by smoking and 18% caused by excess body weight, physical inactivity, excess alcohol consumption, and poor nutrition. Intensive Longitudinal Data (ILD) methods, which collect many assessments captured at high density on a micro-timescale (e.g., seconds, minutes, hours) using real-time data capture methodologies (e.g., mobile sensing and accelerometry), offer enormous opportunities for insight into the dynamic nature of cancer risk factors and outcomes. In ILD studies, it is common to have hundreds to thousands of observations per subject, and this allows us to model intraindividual parameters comprised of time-varying variables such as means (e.g., how unhappy is a subject, on average, across occasions?), variances (e.g., how erratic is a subject’s mood across occasions?), and slopes (e.g., is a subject’s mood related to feelings of energy across occasions?). In our preliminary work, we have begun developing a series of two-stage multilevel statistical models to test intraindividual mean, variance, and slope parameters as predictors, mediators, and moderators of subject-level outcomes. We have shown that greater intraindividual variability in, but not mean of feeling energetic, was associated with lower odds of meeting physical activity guidelines. We have also found that intraindividual variability in positive affect moderated the associations of intraindividual mean positive affect with depressive symptoms and alcohol consumption. However, current data analysis techniques are limited in several key areas, which severely limit our ability to capitalize on the full potential ILD to enhance behavioral and social science research in cancer control. To address these gaps, we propose the following aims. Specifically, we will develop multilevel models capable of (1) estimating effects of intraindividual variances and slopes for time-varying variables nested in time or within clusters of people, (2) predicting cancer control outcomes nested within time or clusters, (3) comparing the relative predictive strength of several intraindividual variances, and (4) examining intraindividual variances or slopes for count or ordinal variables. We will test these statistical extensions by conducting secondary analyses of data from three cancer control studies using ILD (with 94,892 occasions nested within 643 people) including children, middle-aged adults, parent-child dyads. We will also develop, test, and disseminate a stand-alone software with GUI capable of running these statistical models to be used by applied behavioral and social science researchers. The methods to be developed can easily generalize to a variety of other disease areas such asthma, disordered eating, suicide prevention, HIV risk, medication adherence, and environmental exposures.