OBJECTIVES: This paper provides a systematic treatment of the correction for nonrandom sample selection bias of medical cost data where the selection rule is described by a censored regression model. METHODS: The proposed method first uses the duration of time a patient is tracked for the selection, rather than a binary variable, namely whether or not the duration is censored. Second, using Tobit residuals instead of the inverse Mills Ratio allows us to decrease large variances introduced by the Heckman model when there a no exclusion restrictions. RESULTS: We show that the resulting estimators are consistent and asymptotically normal. Simulation studies confirmed our results. Moreover, we derive a simple test to determine possible sample selection bias due to censoring. Data from a study on the medical cost of cancer is used as an application of the method. CONCLUSIONS: We applied OLS, Heckit, Lin[2000], Lin[2003] methods as well as our proposed method to see how they would differ in practice. Lin's methods and the proposed method were most efficient relative to other methods. In addition, an advantage of our method was a test of whether selection bias exists in our data set.