title: On the validity of using increases in 5-year survival rates to measure success in the fight against cancer.
abstract: BACKGROUND
  The 5-year survival rate of cancer patients is the most commonly used statistic to reflect improvements in the war against cancer. This idea, however, was refuted based on an analysis showing that changes in 5-year survival over time bear no relationship with changes in cancer mortality.
  
  
  METHODS
  Here we show that progress in the fight against cancer can be evaluated by analyzing the association between 5-year survival rates and mortality rates normalized by the incidence (mortality over incidence, MOI). Changes in mortality rates are caused by improved clinical management as well as changing incidence rates, and since the latter can mask the effects of the former, it can also mask the correlation between survival and mortality rates. However, MOI is a more robust quantity and reflects improvements in cancer outcomes by overcoming the masking effect of changing incidence rates. Using population-based statistics for the US and the European Nordic countries, we determined the association of changes in 5-year survival rates and MOI.
  
  
  RESULTS
  We observed a strong correlation between changes in 5-year survival rates of cancer patients and changes in the MOI for all the countries tested. This finding demonstrates that there is no reason to assume that the improvements in 5-year survival rates are artificial. We obtained consistent results when examining the subset of cancer types whose incidence did not increase, suggesting that over-diagnosis does not obscure the results.
  
  
  CONCLUSIONS
  We have demonstrated, via the negative correlation between changes in 5-year survival rates and changes in MOI, that increases in 5-year survival rates reflect real improvements over time made in the clinical management of cancer. Furthermore, we found that increases in 5-year survival rates are not predominantly artificial byproducts of lead-time bias, as implied in the literature. The survival measure alone can therefore be used for a rough approximation of the amount of progress in the clinical management of cancer, but should ideally be used with other measures.
authors: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America; Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
authors: Yosef E
authors: YE
authors: Maruvka
authors: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America; Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
authors: Min
authors: M
authors: Tang
authors: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America; Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
authors: Franziska
authors: F
authors: Michor
mesh_terms: D018572:Disease-Free Survival; D006801:Humans; D015994:Incidence; D008403:Mass Screening; D009369:Neoplasms / Q000453:epidemiology* / Q000401:mortality; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012042:Registries; D016019:Survival Analysis; D015996:Survival Rate