New Statistical Models for Intensive Longitudinal Data

密集纵向数据的新统计模型

• 批准号: 7660393
• 负责人: LISA C DIERKER
• 金额: $ 29.32万
• 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/7660393
• 关键词: Address; Alcohol or Other Drugs use; Alcohols; Area; Asthma; Attention; Blood Glucose; Blood Pressure Monitors; Calculi; Charge; Clinical; Collection; Complex; Computer software; Data; Data Collection; Data Set; Dependence; Development; Diabetes Mellitus; Disease; Disease Outcome; Drug abuse; Drug usage; Equation; Esthesia; Etiology; Hand; Handheld Computers; Health; Heart Diseases; Heart Rate; Human; Hyperactive behavior; Individual; Knowledge; Linear Models; Link; Literature; Malignant Neoplasms; Marijuana; Measurement; Measures; Mental Health; Methodology; Methods; Modeling; Monitor; Moods; Online Systems; Outcome; Pennsylvania; Physiology; Plant Roots; Population; Predisposing Factor; Prevention approach; Procedures; Process; Publishing; Research; Research Institute; Research Personnel; Scientist; Smoking; Smoking Cessation Intervention; Smoking Status; Statistical Methods; Statistical Models; Structure; Subgroup; Substance abuse problem; Testing; Time; Tobacco; United States; Universities; Withdrawal; behavioral/social science; data modeling; depression; design; drinking; flexibility; glucose monitor; meter; person centered; physical conditioning; response; social; software development; technological innovation; theories; tool; user friendly software; web site

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的过程，使研究人员能够轻松地收集密集的纵向数据，这些数据具有许多密集的测量场合。密集的纵向数据的收集为更好地理解各种身体健康和精神健康状况的出现和临床过程提供了很大的希望。理论上，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，为利用密集纵向数据的丰富性而设计的统计方法目前落后于这些数据收集能力。例如，目前还不清楚哪些统计程序可以应用于密集的纵向数据，以解决以下问题：戒烟的主观感觉在一天或一周内如何变化？情绪和吸毒之间有什么关系？这种关系会在不同的受试者之间发生变化吗？如果人口是几个子组的组合，这种关系在子组之间是否会有所不同？在本项目中，我们针对密集纵向数据提出了三类新的统计模型，分别具有连续响应、二元响应和计数响应。这些新模型具有许多有价值的特点，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。拟议的新模型允许总体由几个子组组成，影响随着时间的推移和不同受试者的变化而变化，它们使错误过程的结构非常灵活。我们将为新模型提出估算程序，并开发软件来实施拟议的程序。我们计划应用拟议的程序，使用关于烟草、酒精和大麻的经验数据来检验关于药物使用的重要假说，并使用关于哮喘的经验数据来解决健康研究中的重要问题。我们还计划将拟议的研究发表在统计学和行为/社会科学文献上，并通过免费软件向健康研究中的科学家广泛提供新的程序。因此，拟议的研究将为不同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中心科学问题。拟议中的程序将被用来利用烟草、酒精和大麻的经验数据来解决中心毒品使用问题，这些物质在美国使用最广泛，被认为与无数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检验健康研究中的重要假设。哮喘是一个主要的健康问题，因为美国境内据信有1000万哮喘患者。