Probability-Sampling Framework for Modeling the Impact of Time-Varying Covariates

用于建模时变协变量影响的概率抽样框架

基本信息

批准号：
7808901
负责人：
Stephen L Rathbun
金额：
$ 10.72万
依托单位：
UNIVERSITY OF GEORGIA
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-04-01 至 2013-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7808901
关键词：
Accounting Acquired Immunodeficiency Syndrome Addictive Behavior Address Adult Asthma Attention Behavior Behavioral Biometry Blood Pressure Calculi Cancer Patient Cations Characteristics Cigarette Circadian Rhythms Clinical Clinical Trials Collaborations Collection Complex Computer software Data Data Analyses Data Quality Data Set Dependence Development Devices Diagnostic Ecology Electronics Emotional Environment Environmental Health Epidemiology Epilepsy Equation Event Event History Analysis Failure Funding Goals Grant Health Psychology Health Sciences Human Individual Infection Information Technology Institutes Investigation Joints Lead Learning Literature Methods Modeling Monitor Moods National Institute of Drug Abuse National Institute on Alcohol Abuse and Alcoholism Outcome Patients Pattern Pharmacologic Substance Phase Population Probability Probability Samples Procedures Process Property Psychologist Public Health Publications Publishing Questionnaires Recording of previous events Recurrence Recurrent tumor Relapse Reporting Research Research Personnel Risk Risk Factors Sampling Science Seizures Smoke Smoker Smoking Smoking Behavior Statistical Data Interpretation Statistical Methods Stress Survival Analysis Techniques Time Time Series Analysis United States National Institutes of Health Universities Variant Work Writing addiction base behavioral/social science cardiovascular disorder risk chronic pain design diaries dissemination research experience hazard human subject improved innovation interest novel strategies phase 1 study professor psychologic public health relevance racial discrimination response smoking cessation statistics success time interval tool web site

项目摘要

DESCRIPTION (provided by applicant): We propose a new probability-based framework for modeling the impact of time-varying covariates on the timing of repeated discrete behavioral events to support collaborative efforts to model two existing data sets involving Ecological Momentary Assessment (EMA) of smoking. EMA involves the use of electronic diaries to monitor the real-time behavior of subjects in their environments, avoiding recall biases inherent to retrospective questionnaires. Although EMA is increasingly important in the health sciences, aside from our own work little if any attention has been given to EMA in the statistics literature. The specific aims of the proposed collaborative research between a biostatistician and a psychologist are to: 1) Develop a general probability-sampling framework for estimating the impact of time varying- covariates on the pattern of ad-lib smoking, lifetimes to lapse following a designated quit date, and the post-lapse pattern of cigarettes that takes into account temporal dependence among these smoking events with general applications to ecological momentary assessment; 2) Construct joint models for the effects of time-varying covariates and time-of-day, accounting for circadian cycles in addictive behavior; 3) Develop a model for variation among subjects with respect to baseline smoking rates, effects of time-covariates, and time of day, from which clusters of subjects showing similar smoking behaviors may be identified; and 4) Construct models in which the hazard of smoking a cigarette at a given instant in time depends not only on the current values of time-varying covariates, but also on an integrated function of past values of those covariates. To obtain a better understanding of the mechanisms underlying success or failure of attempts to quit smoking, point process and survival models will be constructed to describe the impact of temporal variation in smokers' psychological states and environments on the pattern of ad lib smoking, lifetime to lapse following smoking cessation and the post lapse pattern of cigarettes leading to relapse. A common feature of both point process and survival models is that the full likelihood involves the integration of a function (intensity or hazard) of the time- varying covariates over the sampling domain. The proposed framework treats the sampling domain as a population of points, and assumes that the covariates are an unknown but deterministic function of time. A probability-based design is used to sample the covariates, from which a design-unbiased estimator of the integrated function of the covariates may be obtained. Substituting this design-unbiased estimator into the likelihood yields an objective function that may be maximized to obtain the proposed estimator for the model parameters. Design-based inference for the integrated function of the covariates is offered as an alternative to a hierarchical modeling approach based on joint modeling of the time-varying covariates and the timing of repeated behavioral events. In contrast to the hierarchical approach, no model assumptions are required regarding the time-varying covariates. PUBLIC HEALTH RELEVANCE: To obtain a better understanding of the mechanisms underlying success or failure of attempts to quit smoking, we propose to develop new statistical methods for analyzing two existing data sets involving the use of electronic diaries to monitor the moods and environments of smokers in real time. Beyond the smoking data considered here, the proposed methods have broad applications in public health, ranging from analysis of addictive behaviors to investigations of patterns of asthma attacks, epileptic seizures, recurrent tumors in cancer patients, and more.

描述（由申请人提供）：我们提出了一个新的基于概率的框架，用于建模时变协变量对重复离散行为事件时间的影响，以支持协作努力对涉及生态瞬时评估（EMA）的两个现有数据集进行建模吸烟。 EMA 涉及使用电子日记来监控受试者在其环境中的实时行为，避免回顾性调查问卷固有的回忆偏差。尽管 EMA 在健康科学中越来越重要，但除了我们自己的工作之外，统计文献中很少对 EMA 给予关注。生物统计学家和心理学家之间拟议的合作研究的具体目标是： 1) 开发一个通用的概率抽样框架，用于估计时变协变量对随意吸烟模式、指定吸烟时间后的寿命的影响。戒烟日期和戒烟后模式，考虑到这些吸烟事件之间的时间依赖性，一般应用于生态瞬时评估； 2）构建时变协变量和一天中时间的影响的联合模型，考虑成瘾行为的昼夜节律周期； 3) 开发一个模型，用于研究受试者在基线吸烟率、时间协变量的影响和一天中的时间方面的变化，从中可以识别出表现出相似吸烟行为的受试者群； 4) 构建模型，其中在给定时刻吸烟的危险不仅取决于时变协变量的当前值，还取决于这些协变量过去值的积分函数。为了更好地理解戒烟成功或失败的机制，将构建点过程和生存模型来描述吸烟者心理状态和环境的时间变化对随意吸烟模式、终生吸烟模式的影响。戒烟后的戒烟以及导致复吸的戒烟后模式。点过程和生存模型的一个共同特征是，完全似然涉及采样域上时变协变量的函数（强度或风险）的积分。所提出的框架将采样域视为点的总体，并假设协变量是未知但确定的时间函数。基于概率的设计用于对协变量进行采样，从中可以获得协变量的积分函数的设计无偏估计量。将此设计无偏估计量代入似然度会产生一个目标函数，该目标函数可被最大化以获得模型参数的建议估计量。提供基于设计的协变量集成函数推断，作为基于时变协变量和重复行为事件时间的联合建模的分层建模方法的替代方案。与分层方法相反，不需要关于时变协变量的模型假设。公共卫生相关性：为了更好地了解戒烟尝试成功或失败的机制，我们建议开发新的统计方法来分析两个现有数据集，其中涉及使用电子日记来监测吸烟者的情绪和环境即时的。除了这里考虑的吸烟数据之外，所提出的方法在公共卫生领域也有广泛的应用，从分析成瘾行为到研究哮喘发作、癫痫发作、癌症患者复发性肿瘤等模式。