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

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

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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，以建模时变协变量对重复离散行为事件的时间的影响，以支持协作努力，以模拟两个现有的数据集，该数据集涉及涉及吸烟的生态瞬时评估（EMA）。 EMA涉及使用电子日记来监视受试者在其环境中的实时行为，避免回顾性问卷固有的召回偏见。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，如果在统计文献中对EMA有任何关注，那么我们的工作很少。生物统计学家和心理学家之间提出的合作研究的具体目的是：1）建立一个一般的概率采样框架，以估算时间变化的时间对AD-LIB吸烟模式的影响，在指定的戒烟日期中，以及在这些烟雾后的情况下，在这些烟雾后的应用程序中，这些临时性的依赖性依赖于这些烟雾的依赖性，并在这些情况下进行了这些烟雾的依赖。 2）构建关节模型，以造成时间变化的协变量和时间的效果，这是昼夜行为中成瘾行为的循环； 3）为基线吸烟率，时间互动的影响以及一天中的时间开发一个模型，以在受试者之间变化，从中可以确定出表现出相似吸烟行为的受试者的簇； 4）构造模型，其中在给定的瞬间吸烟的危害不仅取决于当前变化的协变量的当前值，还取决于这些协变量的过去值的综合功能。为了更好地了解戒烟的成功或失败的机制，将构建点过程和生存模型，以描述吸烟者心理状态和环境中时间变化对AD LIB吸烟模式的影响，终生对吸烟和戒烟后的终生消失以及导致复发的卷烟的消失模式。点过程和生存模型的一个共同特征是，完全可能性涉及在采样域上随时间变化的协变量的函数（强度或危险）的整合。所提出的框架将采样域视为点群，并假定协变量是时间的未知但确定性的功能。基于概率的设计用于采样协变量，可以从中获得对协变量综合函数的设计均匀估计器。将此稳定的估计量代替可能会产生一个目标函数，该目标函数可以最大化，以获得模型参数的拟议估计器。基于设计的协变量综合功能的推断是基于时间变化协变量的联合建模和重复行为事件的时机的层次建模方法的替代方法。与分层方法相反，关于时变的协变量，不需要模型假设。公共卫生相关性：为了更好地理解成功或戒烟试图失败的机制，我们建议开发新的统计方法，以分析两个现有的数据集，涉及使用电子日记来实时监视吸烟者的情绪和环境。除了此处考虑的吸烟数据外，所提出的方法在公共卫生中还广泛应用，从对成瘾行为的分析到对哮喘发作，癫痫发作，癌症患者的复发性肿瘤的调查等。