OBJECTIVES: Progression-free survival (PFS) is frequently used to establish the clinical efficacy of anti-cancer drugs. However, this surrogate measure of survival is of limited interest for the economic evaluation of these treatments. Therefore, the aim of this study is to develop a predictive model for OS based on PFS data in the context of metastatic breast cancer (mBC), which would be suitable for cost-effectiveness (cost per life-year saved) and cost-utility analyses. METHODS: A systematic review of the literature was conducted according to the PICO method: Population consisted of women with mBC; Interventions and Comparators were standard treatments for mBC or best supportive care; Outcomes of interest were median PFS and median OS. All selected studies were randomized trials published from 1990 to 2010. Two independent reviewers screened titles, abstracts, and full papers for eligibility. Then, reviewers independently extracted data from selected studies (median PFS, median OS, and potentially predictive covariates). The relationship between PFS and OS was assessed by calculating Pearson’s correlation coefficient. Finally, statistical analyses (ANOVA and Pearson’s correlation) were performed to identify covariates having a significant impact on OS. RESULTS: A total of 5041 studies were identified and 151 fulfilled the eligibility criteria. According to the data extracted from selected studies, there is a significant relationship between median PFS and median OS (r=0.373;p<0.01). Moreover, many covariates have a statistically significant impact on OS including age (p<0.01), type of treatment (p<0.01), line of treatment (p<0.01), ECOG status (p<0.01), and number and sites of metastasis (p<0.01). CONCLUSIONS: Results of this systematic review point toward a significant relationship between PFS and OS in the context of mBC. These findings will enable the development of a predictive model for OS based on PFS and significant covariates, which will eventually bring answers to an important challenge in the economic evaluation of anti-cancer drugs.