A Multivariate Probit Model for Health Services Research

卫生服务研究的多元概率模型

• 批准号: 6820885
• 负责人: HUA YUN CHEN
• 金额: $ 20.68万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-21 至 2007-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6820885
• 关键词: health care service utilization; health science research; human data; longitudinal human study; mathematical model; mental health services; model design /development; statistics /biometry

DESCRIPTION (provided by applicant): A central theme in mental health services research is the characterization of patterns of service utilization and the identification of their determinants. For example, there is considerable interest in the effects of changes in the health care delivery system on utilization of inpatient, outpatient, emergency room, and home mental health care services. Although considerable statistical worlc in this area has been conducted (see Gibbons 2001 for an overview), little work, ifany, has been done to incorporate both the longitudinal and multivariate nature of health services research data. While some investigators have proposed approaches to the analysis of multivariate patterns of service use in cross-sectional data, and others have proposed longitudinal approaches for the analysis of a single service use repeatedly measured over time, very little if any work has been done on the analysis of longitudinall; measured multivariate service utilization data. Simple piecemeal univariate analyses ignore the correlated and often compensatory nature of service utilization data (eg. health care system changes that lead to decreases in emergency room use may lead to increase in outpatient treatment use). In this study, we will develop a general mixed-effects multivariate probit regression model for the simultaneous analysis of repeatedly measured multivariate binary data. Correlations between multiple binary measures at a single point in time are modeled as a factor analytic process, and correlation among the repeated measurements over time are modeled as : random-effects process. The net result is that we can now model the effects of design variables (e.g, changes in the health care delivery system) and case mix variables (e,g., age, sex, and race) on multivariate utilization patterns. Generalizations of the mode will include, extension to ordinal response data (e.g., no use, mild use, moderate use, high use, or O visits, I, visit, 2 visits, 3 or more visits), mixtures of discrete and continuous responses (e.g., the joint analysis of service utilization and cost), and extension to a multivariate logistic regression model. An integral part of the project will be to both explore and develop alternative approaches to likelihood evaluation (fixed-point and adaptive quadrature, Laplace approximation, and Monte Carlo integration), parameter estimation (Newton Raphson, Fisher scoring, and the EM algorithm), and hypothesis testing. A large-scale simulation study will b conducted to study the statistical properties of the general model and various alternative formulations. Finally, the model will be applied in the analysis of data collected by Dr. Margarita Alegria at the University of Puerto Rico on the effects of health care reform on longitudinal mental health services utilization. In addition to development of the statistical theory and estimation procedure, we propose to develop a WINDOWS based freeware computer program, MIXMVP, to be distributed from the MIXREG/MIXOR web site www.uic.edu/labs/biostat. No general multivariate probit regression software is currently available.

描述（由申请人提供）：精神卫生服务研究的一个中心主题是服务利用模式的表征及其决定因素的识别。例如，有相当大的兴趣，在医疗保健提供系统的变化对利用住院，门诊，急诊室和家庭精神卫生保健服务的影响。虽然在这一领域进行了大量的统计分析（见Gibbons 2001年的概述），但几乎没有做任何工作来纳入卫生服务研究数据的纵向和多变量性质。虽然一些调查人员提出了方法来分析的多变量模式的服务使用的横截面数据，和其他人提出了纵向的方法来分析一个单一的服务使用重复测量随着时间的推移，非常少，如果有任何工作已经做的分析，测量的多变量服务利用数据。简单的逐段单变量分析忽略了服务利用数据的相关性和补偿性（例如，导致急诊室使用减少卫生保健系统变化可能导致门诊治疗使用增加）。在本研究中，我们将发展一个通用的混合效应多元概率单位回归模型，以同时分析重复测量的多元二进制数据。在一个单一的时间点上的多个二元测量之间的相关性建模为一个因素分析过程，并在一段时间内的重复测量之间的相关性建模为：随机效应过程。最终结果是，我们现在可以模拟设计变量（例如，医疗保健提供系统的变化）和病例组合变量（例如，年龄、性别和种族）对多变量利用模式的影响。模式的推广将包括扩展到有序响应数据（例如，不使用、轻度使用、中度使用、高度使用或0次访问、1次访问、2次访问、3次或更多次访问），离散和连续响应的混合（例如，服务利用率和成本的联合分析），并扩展到多元逻辑回归模型。该项目的一个组成部分将是探索和开发可能性评估（定点和自适应求积、拉普拉斯近似和蒙特卡罗积分）、参数估计（牛顿拉夫森、费舍尔评分和EM算法）和假设检验的替代方法。将B大规模模拟研究，以研究一般模型和各种替代配方的统计特性。最后，该模型将被应用于分析收集的数据，由博士玛格丽塔阿莱格里亚在波多黎各大学的影响，医疗保健改革纵向精神卫生服务的利用。除了统计理论和估计程序的发展，我们建议开发一个基于WINDOWS的免费计算机程序，MIXMVP，从MIXREG/MIXOR网站www.uic.edu/labs/biostat分发。目前还没有通用的多变量概率单位回归软件。