Austin P C, Escobar M, Kopec J A
Institute for Clinical Evaluative Sciences, North York, Ontario, Canada.
Qual Life Res. 2000;9(8):901-10. doi: 10.1023/a:1008938326604.
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.
自我报告的健康状况通常使用心理测量或效用指数进行衡量，这些指数提供一个分数，旨在总结个人的健康状况。健康状况测量可能会受到天花板效应的影响。研究人员经常希望考察健康决定因素与健康状况测量之间的关系。忽略天花板效应的存在或健康状况测量中的审查情况的回归方法可能会产生有偏差的系数估计。托比特回归模型是计量经济学研究中用于对审查变量进行建模的常用工具。作者进行了一项蒙特卡罗模拟研究，以对比托比特模型对审查数据的表现与普通最小二乘法（OLS）回归的表现。结果表明，在存在天花板效应的情况下，如果健康状况测量的条件分布具有均匀方差，那么与OLS回归的估计相比，托比特模型的系数估计具有更好的表现。然而，如果条件分布具有非均匀方差，那么托比特模型的表现至少与OLS模型一样差。
