Pharmacoeconomic evaluation can assess costs by using controlled trials. Statistical analysis of costs follow a normal (N), lognormal (LN) or survival (S) model. Each model assumes different statistical properties that may introduce bias. If one pharmaceutical is less expensive and more effective than another, then no incremental cost-effectiveness ratio is calculated. Therefore, economically attractive pharmaceuticals may not be identified if a biased method of cost analysis is used. OBJECTIVE: The purpose of this study was to 1) evaluate potential bias in methods of cost analysis and 2) demonstrate an unbiased method. METHOD: Cost models were considered unbiased if they accounted for: 1) independent variables, 2) disease-specific death, 3) unrelated death 4) dropouts, and 5) informative censoring. Potential differences in statistical significance were demonstrated by simulating 12-month cost data from the GUSTO study that compared use of streptokinase and tissue plasminogen activator (TPA). We simulated n=10,000 from a Gamma distribution for each therapy, and 5% incidences of death, unrelated death, or dropouts. RESULTS: N and LN only account for independent variables. Kaplan-Meier (KM) does not account for independent variables; but competing risk (CR) accounts for all factors. For streptokinase and TPA, mean actual costs were $24,575 and $24,990; mean simulated costs were $24,575 and $24,990. Costs of streptokinase versus TPA were significantly different under N (mean difference [95% confidence interval]=$415 [277 - 554], p<0.001), and under LN ($431 [289- 547], p<0.001) For S models, costs were not significantly different under KM (hazard ratio for costs [95% confidence interval]=0.94 [0.89- 1.01], p=0.08), or CR (0.96 [0.91 - 1.01], p=0.09). The robustness of results and goodness of fit depend upon the actual distribution of costs. CONCLUSION: A competing risk model is an unbiased and comprehensive method of cost analysis. Further research is required to assess the clinical importance of alternate methods of cost analysis.