The use of the Tobit model for analyzing measures of health status

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. Frequent ly, researchers want to examine relationships between determinants of healt h and measures of health status. Regression methods that ignore the presenc e of a ceiling effect, or of censoring in the health status measurements ca n produce biased coefficient estimates. The Tobit regression model is a fre quently used tool for modeling censored variables in econometrics research. The authors carried out a Monte-Carlo simulation study to contrast the per formance of the Tobit model for censored data with that of ordinary least s quares (OLS) regression. It was demonstrated that in the presence of a ceil ing 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. Howe ver, if the conditional distribution had non-uniform variance, then the Tob it model performed at least as poorly as the OLS model.