New Statistical Models for Intensive Longitudinal Data

密集纵向数据的新统计模型

• 批准号: 7363879
• 负责人: LISA C DIERKER
• 金额: $ 29.69万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-26 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7363879
• 关键词: Address; Alcohol or Other Drugs use; Alcohols; Area; Asthma; Attention; Blood Glucose; Blood Pressure Monitors; Calculi; Cations; Charge; Class; Clinical; Collection; Complex; Computer software; Condition; Count; Data; Data Collection; Data Set; Dependence; Development; Diabetes Mellitus; Disease; Disease Outcome; Disease regression; Drug abuse; Drug usage; Equation; Esthesia; Etiology; Hand; Handheld Computers; Health; Heart Diseases; Heart Rate; Human; Hyperactive behavior; Individual; Internet; Knowledge; Linear Models; Link; Literature; Malignant Neoplasms; Marijuana; Measurement; Measures; Mental Depression; Mental Health; Methodology; Methods; Modeling; Monitor; Moods; Numbers; Online Systems; Outcome; Pennsylvania; Physiology; Plant Roots; Population; Predisposing Factor; Prevention approach; Procedures; Process; Publishing; Range; Research; Research Institute; Research Personnel; SAS; Scientist; Site; Smoking; Smoking Cessation Intervention; Smoking Status; Statistical Methods; Statistical Models; Structure; Subgroup; Substance abuse problem; Testing; Thinking; Time; Tobacco; United States; Universities; Week; Withdrawal; behavioral/social science; data modeling; day; design; drinking; glucose monitor; meter; person centered; response; social; software development; technological innovation; theories; tool; user friendly software

DESCRIPTION (provided by applicant): Technological innovations have revolutionized the process of scientific research and knowledge discovery in health studies and allow researchers to easily collect intensive longitudinal data that have many closely spaced measurement occasions. The collection of intensive longitudinal data holds much promise for better understanding the emergence and clinical course of a wide range of both physical health and mental health conditions. In theory, intensive longitudinal data can provide answers to important questions in health studies. However, statistical methodology designed to capitalize on the richness of intensive longitudinal data currently lags behind these data collection abilities. For instance, it is not immediately clear that what statistical procedures can be applied to intensive longitudinal data to address questions such as: How does the subjective sensation of withdrawal vary over a day or a week? What is the relationship between mood and drug use? Does the relationship change across individual subjects? Does the relationship vary across subgroups if the population is a combination of several subgroups? In this project, we propose three new classes of statistical models for intensive longitudinal data with a continuous response, binary response and count response, respectively. These new models possess many valuable features which make them the most appropriate to use for addressing critical questions in health studies and drug abuse researches. The proposed new models allow populations to be composed of several subgroups, and effects to vary over time and change across individual subjects, and they keep the structure of the error process very flexible. We will propose estimation procedures for the new models, and develop software to implement the proposed procedures. We plan to apply the proposed procedures to test important hypotheses about drug use using empirical data on tobacco, alcohol and marijuana, and address important questions in health studies using empirical data on asthma. We also plan to publish the proposed research in both the statistical and behavioral/social science literature, and make the new procedures widely available to scientists in health studies, by means of software free of charge. Thus, the proposed research will provide scientists in various health-related research areas with tools they need to address central scientific questions using intensive longitudinal data. The proposed procedures will be employed to address central drug use questions using empirical data on tobacco, alcohol and marijuana, which are the most widely used substances within the US and have been linked to a myriad of both short and long-term consequences. The proposed procedures will also be used to test important hypotheses in health studies using empirical data on asthma, which is a major health problem as there are thought to be 10 million people with asthma within the United States.

描述（由申请人提供）：技术创新彻底改变了健康研究中的科学研究和知识发现过程，并使研究人员能够轻松收集具有许多紧密间隔测量场合的密集纵向数据。密集的纵向数据的收集为更好地了解各种身体健康和心理健康状况的出现和临床过程提供了很大的希望。从理论上讲，密集的纵向数据可以为健康研究中的重要问题提供答案。然而，旨在利用密集的纵向数据的丰富性的统计方法目前落后于这些数据收集能力。例如，目前还不清楚什么样的统计程序可以应用于密集的纵向数据，以解决这样的问题：在一天或一周内，戒断的主观感觉是如何变化的？情绪与药物使用之间的关系是什么？这种关系在个体受试者之间是否会发生变化？如果总体是几个亚组的组合，那么亚组之间的关系是否不同？在这个项目中，我们提出了三类新的统计模型，密集的纵向数据与连续响应，二进制响应和计数响应，分别。这些新模型具有许多有价值的功能，使它们最适合用于解决健康研究和药物滥用研究中的关键问题。提出的新模型允许人口由几个亚组组成，影响随时间变化，并在个体受试者之间发生变化，并且它们保持了错误过程的结构非常灵活。我们将提出新模型的估计程序，并开发软件来实现所提出的程序。我们计划应用所提出的程序来测试使用烟草，酒精和大麻的经验数据的药物使用的重要假设，并使用哮喘的经验数据解决健康研究中的重要问题。我们还计划在统计和行为/社会科学文献中发表拟议的研究，并通过免费软件向健康研究科学家广泛提供新程序。因此，拟议的研究将为各个健康相关研究领域的科学家提供他们需要的工具，以使用密集的纵向数据来解决核心科学问题。拟议的程序将用于使用烟草，酒精和大麻的经验数据来解决中心药物使用问题，这些药物是美国使用最广泛的物质，并与无数的短期和长期后果有关。拟议的程序还将用于使用哮喘的经验数据来测试健康研究中的重要假设，哮喘是一个主要的健康问题，因为据信美国有1000万人患有哮喘。