OBJECTIVES: If proportional hazards assumption holds, Cox regression allows for estimation of treatment effect in the form of hazard ratio. The common practice is to fit parametric model to control arm, then to apply hazard ratio to predict treatment arm. However proportional hazards assumption is rarely verified. Our aim was to estimate how proportional hazards assumption may impact cost-effectiveness. METHODS: Markov model was developed to describe cancer patients treatment. Health states distinguished in the model were: progression‑free, progression and death. Time to progression and death were obtained from clinical trials for breast and renal cell cancer and implemented into the model on the basis of Weibull curves, fitted to data from clinical studies. Calculations were carried out separately with or without using given hazard parameters. It was assumed that compared interventions differ only in terms of time to progression or death. All the other parameters were the same for both arms. RESULTS: In case of renal cell carcinoma bisphosphonates combined with sunitinib were compared with sunitinib alone. When time to progression differs between interventions the average time spent by patient in progression-free state was 1.39 vs 0.72 and 2.01 vs 0.72 years with and without proportional hazards assumption, what lead to differences in QALY of 0.20 and 0.39 respectively. When time to death differs between interventions the average survival was 5.17 vs 2.65 years and 5.41 vs 2.65 years with and without proportional hazards assumption and that resulted in differences in QALY of 1.01 and 1.11. CONCLUSIONS: These results indicate that, taking costs into account, proportional hazard assumption may have large impact on cost-effectiveness. Proportional hazards assumption should be always checked and its impact on obtained results should be estimated in sensitivity analysis.