Measurement Error in Latent Class Models of Adherence

依从性的潜在类别模型中的测量误差

基本信息

批准号：
8009816
负责人：
Knashawn Hodge Morales
金额：
$ 13.78万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-02-01 至 2012-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8009816
关键词：
Accounting Adherence Affect Age Antidepressive Agents Area Award Cardiovascular Diseases Clinical assessments Data Development Elderly Epidemiologic Studies Equation Funding Gender Goals Growth Guidelines Health Services Research Heterogeneity Hybrids Intervention Intervention Studies Investigator-Initiated Research Knowledge Lead Learning Literature Measurement Measures Mental Depression Mental Health Services Mental disorders Mentored Research Scientist Development Award Mentors Methodology Methods Modeling National Institute of Mental Health Outcome Patient Self-Report Patients Pattern Phase Primary Health Care Race Randomized Relative (related person)Research Research Methodology Sampling Scientist Screening procedure Site Statistical Methods Structural Models Suicide prevention System Treatment Effectiveness United States National Institutes of Health Validation base career career development collaborative trial design education planning effective therapy effectiveness trial experience follow-up method development non-compliance pill randomized trial response tool treatment as usual treatment effect

项目摘要

DESCRIPTION (provided by applicant): This application for an NIMH Mentored Research Scientist Career Development (K01) award entitled "Measurement error in latent class models of adherence" seeks support to provide an intensive, mentored research experience, culminating in a successful investigator-initiated research award application and launching a career as an academic biostatistician focusing on developing statistical methods for difficult methodological problems affecting the validity of mental health services research. As part of the education plan, the PI will increase her understanding of mental disorders and the systems used to collect services research data and explore a new area of statistical methods (latent class and latent variable methods). The four-phase research plan focuses on measurement error, latent class and latent variable methods for research questions generated from the NIMH study of "Prevention of Suicide in Primary Care Elderly: Collaborative Trial," but apply to data from randomized trials in general. The conventional "measurement models" under the SEM framework do not naturally accommodate validation data from a sub-sample. The classical and Berkson measurement models that appear in the statistical literature, offer different ways of incorporating validation data as a measurement error variance estimate like a variance component. The goals of the K and my future research focuses on incorporating these different measurement error approaches. Phase 1 is designed to understand how patterns of adherence are related to depression trajectories and how the tow influence treatment effects. This phase will focus on generating knowledge of latent class and latent variable methodology with application to the PROSPECT study. Phase 2 focuses on estimating measurement error in the measures of adherence. Phase 3 uses results from Phases 1 and 2 to adjust latent class/variable results for error in the measurement of adherence. Phase 4 will use everything learned in Phase 1, 2, and 3 to guide the development of a NIH R01 for statistical methods that accurately accommodate measurement error. In addition, Bayesian model averaging is proposed to account for model error by eliminating the choice of the number of latent classes. This research plan will provide useful tools that will aid in the design and analysis of other mental health services studies.

描述（由申请人提供）：本申请题为“依从性潜在类别模型中的测量误差”，申请 NIMH 指导研究科学家职业发展 (K01) 奖，寻求支持以提供密集的、指导性的研究经验，最终促成研究者发起的成功研究研究奖申请并开启学术生物统计学家的职业生涯，专注于为影响心理健康服务研究有效性的困难方法问题开发统计方法。作为教育计划的一部分，PI 将加深对精神障碍和用于收集服务研究数据的系统的了解，并探索统计方法的新领域（潜在类别和潜在变量方法）。四阶段研究计划的重点是测量误差、潜在类别和潜在变量方法，以解决 NIMH 的“初级保健老年人自杀预防：协作试验”研究中产生的研究问题，但一般适用于随机试验的数据。 SEM 框架下的传统“测量模型”自然不能容纳来自子样本的验证数据。统计文献中出现的经典测量模型和伯克森测量模型提供了将验证数据合并为测量误差方差估计（如方差分量）的不同方法。 K 和我未来研究的目标集中于整合这些不同的测量误差方法。第一阶段旨在了解依从模式如何与抑郁症轨迹相关，以及这两种模式如何影响治疗效果。此阶段将重点关注生成潜在类别和潜在变量方法的知识，并将其应用于 PROSPECT 研究。第二阶段的重点是估计依从性测量中的测量误差。第 3 阶段使用第 1 阶段和第 2 阶段的结果来调整潜在类别/变量结果，以消除依从性测量中的错误。第 4 阶段将利用第 1、2 和 3 阶段学到的所有知识来指导 NIH R01 的开发，以实现准确适应测量误差的统计方法。此外，提出了贝叶斯模型平均，通过消除潜在类别数量的选择来解决模型误差。该研究计划将提供有用的工具，有助于其他心理健康服务研究的设计和分析。