The methodology of cost-effectiveness studies that use a lifetime perspective is based on the extrapolation to infinity of the survival curves. However, the research in this methodological area is at an initial phase. Hence, adequate techniques for survival curve extrapolation still need to be devised for handling the different clinical settings that can be analysed by cost-effectiveness survival studies. After a brief overview of the two most commonly used extrapolation methods (Markov decision-tree model and Gompertz technique), we describe a new method for expressing lifetime survival in cost-effectiveness studies that evaluate cancer patients. Our method extrapolates to infinity a traditional survival curve by assigning a normal life expectancy to patients (or long-term survivors). In this way, the value of mean lifetime survival (MLS) for the patient cohort under study can be determined using a lifetime perspective. This value can be employed in lifetime cost-effectiveness analyses that compare different forms of intervention for that disease condition. A separate section of our method compares the overall survival pattern of cured and not cured patients with that of a reference healthy population to assess the impact of the disease on life expectancy. As an example of the application of our method, we reanalysed a survival data set reported by Spinolo et al in 1992, that refers to patients with acute leukaemia who relapsed after their first allogeneic bone marrow transplantation and who received a second transplant (n=17, mean age at relapse = 26 years). The use of our extrapolation method provided the following results: MLS for leukaemia patients = 105.9 months per patient or 8.8 years per patient; MLS for the reference cohort of healthy subjects = 583.8 months per patient or 48.6 years per patient. We conclude that the extrapolation technique described herein can be useful to handle lifetime survival data in cost-effectiveness analysis.