Consistent Estimation of Binary Models of Mental Illness

精神疾病二元模型的一致性估计

• 批准号: 6596516
• 负责人: ELIZABETH A SAVOCA
• 金额: $ 6.8万
• 依托单位: SMITH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-01 至 2005-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6596516
• 关键词: bias; data collection methodology /evaluation; disease /disorder proneness /risk; health behavior; mental disorders; method development; statistics /biometry

This project will develop econometric methods which yield consistent estimates of binary models of psychiatric illness when the health indicator is subject to error. This topic is highly relevant to health applications since illness is often measured by a binary variable indicating the presence or absence of a diagnosis. Much is known about the econometric consequences of measurement error in control and outcome variables that are continuous. Only recently have researchers recognized that the theoretical implications of measuring a continuous variable with error do not extend to the situation where the mismeasured variable is dichotomous. In particular, contrary to the classical result, even purely random measurement error in a binary outcome variable will bias the coefficients on control variables in both linear and nonlinear regression models. Moreover, the classic textbook econometric solution for eliminating measurement error bias arising from a mismeasured control variable, the instrumental variables approach, is not a valid solution when the variable subject to error is binary. The methods will be developed in the context of two general applications: one where psychiatric diseases serve as control variables--the labor market consequences of mental illness; and one where mental health is the outcome variable--the risk factors for various psychiatric disorders. The methods will rely on both linear and nonlinear regression techniques as well as the maximum likelihood approach. All approaches will be tractable and have broad applicability to any empirical analysis of health-related behavior. The primary data source will be the National Comorbidity Survey.

本项目将开发计量经济学方法，在健康指标存在误差的情况下，对精神疾病的二元模型进行一致的估计。这一主题与健康应用高度相关，因为疾病通常是通过指示诊断存在或不存在的二元变量来测量的。关于连续控制变量和结果变量的测量误差的计量经济学后果已经知道很多了。直到最近，研究人员才认识到，测量一个有误差的连续变量的理论含义并不延伸到被错误测量的变量是二分的情况。特别是，与经典的结果相反，即使是纯粹的随机测量误差在一个二元结果变量将偏向控制变量的线性和非线性回归模型的系数。此外，经典的教科书计量经济学解决方案，消除测量误差偏差所产生的一个错误的控制变量，工具变量的方法，是不是一个有效的解决方案时，受误差的变量是二进制的。这些方法将在两个一般应用的背景下开发：一个是精神疾病作为控制变量-精神疾病的劳动力市场后果;另一个是精神健康作为结果变量-各种精神疾病的风险因素。这些方法将依赖于线性和非线性回归技术以及最大似然法。所有的方法都是易于处理的，并具有广泛的适用性，任何与健康有关的行为的实证分析。主要数据来源是科摩罗全国调查。