OBJECTIVES: Methods from the traditional survival analysis are not directly applicable to estimate medical costs since patients accumulate costs with different rate functions over time, leading to negatively biased estimates. A number of authors have incorporated inverse probability weightiness (IPW) technique to correct for this bias. None of these authors, however, compare their result with the method, which supposedly yields bias estimates, i.e. OLS over uncensored observations. In this paper, we test the differences between the coefficient estimates of OLS over uncensored observations and that of proposed model to determine whether using weight yields statistically different results. Moreover, we compare the estimation power of the proposed alternative models. METHODS: A Hausman kind of test is proposed to compare the weighted estimator and unweighted estimators. Predictive Power tests are used to choose between alternative models. RESULTS: Our data set consists of an inception cohort of 773 patients with incident cases of prostate, colon, lung and breast cancer from 24 Michigan community hospitals and their affiliated oncology units between the years 1994 -1997. Hausman test indicated the results are statistically different. Predictive Power tests yield that Lin[2003] model is better than Lin[2000], Carrides et al. [2000] and Bang and Tsiatis [2000]. CONCLUSION: Two conclusions are as follows: 1. If the error terms are homoskedastic and we fail to reject Hausman test use unweighted simple OLS over complete observations. 2. Otherwise, weighted estimators yield consistent results and predictive power tests can be used to choose among them.