The use of the Tobit model for analyzing measures of health status
The use of the Tobit model for analyzing measures of health status
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DOI：
10.1023/a:1008938326604
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发表时间：
2000-01-01
影响因子：
3.5
通讯作者：
Kopec, JA
中科院分区：
文献类型：
--
作者：
Austin, PC;Escobar, M;Kopec, JA
自我报告的健康状况通常是使用心理测量或效用指数来衡量的，这些指数提供了一个旨在总结个人健康状况的分数。健康状况的衡量可能会受到天花板效应的影响。研究人员经常想要检查健康决定因素和健康状况衡量标准之间的关系。在健康状况测量中忽略天花板效应或审查的回归方法可能会产生有偏差的系数估计。在计量经济学研究中，Tobit回归模型是对删失变量进行建模的常用工具。采用蒙特卡罗模拟方法，对删失数据的Tobit模型和普通最小二乘回归的性能进行了比较。研究表明，在存在天花板效应的情况下，如果健康状况的条件分布具有一致的方差，则由Tobit模型估计的系数比从OLS回归估计的系数具有更好的性能。然而，如果条件分布具有非均匀的方差，那么Tobit模型的表现至少与OLS模型一样差。
Self-reported health status is often measured using psychometric or utility indices that provide a score intended to summarize an individual's health. Measurements of health status can be subject to a ceiling effect. Frequently, researchers want to examine relationships between determinants of health and measures of health status. Regression methods that ignore the presence of a ceiling effect, or of censoring in the health status measurements can produce biased coefficient estimates. The Tobit regression model is a frequently used tool for modeling censored variables in econometrics research. The authors carried out a Monte-Carlo simulation study to contrast the performance of the Tobit model for censored data with that of ordinary least squares (OLS) regression. It was demonstrated that in the presence of a ceiling effect, if the conditional distribution of the measure of health status had uniform variance, then the coefficient estimates from the Tobit model have superior performance compared with estimates from OLS regression. However, if the conditional distribution had non-uniform variance, then the Tobit model performed at least as poorly as the OLS model.