title: Estimation and external validation of a new prognostic model for predicting recurrence-free survival for early breast cancer patients in the UK.
abstract: BACKGROUND
  We aimed to estimate and externally validate a new UK-specific prognostic model for predicting the long-term risk of a first recurrent event (local recurrence, metastatic recurrence, or second primary breast cancer) in women diagnosed with early breast cancer.
  
  
  METHODS
  Using data on the prognostic characteristics and outcomes of 1844 women treated for early breast cancer at the Churchill Hospital in Oxford, parametric regression-based survival analysis was used to estimate a prognostic model for recurrence-free survival. The model, which incorporated established prognostic factors, was externally validated using independent data. Its performance was compared with that of the Nottingham Prognostic Index (NPI) and Adjuvant! Online.
  
  
  RESULTS
  The number of positive axillary lymph nodes, tumour grade, tumour size and patient age were strong predictors of recurrence. Oestrogen receptor (ER) positivity was shown to afford a moderate protective effect. The model was able to separate patients into distinct prognostic groups, and predicted well at the patient level, mean Brier Accuracy Score=0.17 (s.e.=0.004) and overall C=0.745 (95% CI, 0.717-0.773). Its performance diminished only slightly when applied to a second independent data set. When compared with the NPI, the model was able to better discriminate between women with excellent and good prognoses, and it did not overestimate 10-year recurrence-free survival to the extent observed for Adjuvant! Online.
  
  
  CONCLUSIONS
  The model estimated here predicts well at both the individual patient and group levels, and appears transportable to patients treated at other UK hospitals. Its parametric form permits long-term extrapolation giving it an advantage over other prognostic tools currently in use. A simple point scoring system and reference table allow 5-, 10-, and 15-year predictions from the model to be quickly and easily estimated. The model is also available to download as an interactive computer program.
authors.affiliation: Health Economics Research Centre, Department of Public Health, University of Oxford, Old Road Campus, Headington, Oxford OX3 7LF, UK. helen.campbell@dphpc.ox.ac.uk
authors.forename: H E
authors.initials: HE
authors.lastname: Campbell
authors.forename: A M
authors.initials: AM
authors.lastname: Gray
authors.forename: A L
authors.initials: AL
authors.lastname: Harris
authors.forename: A H
authors.initials: AH
authors.lastname: Briggs
authors.forename: M A
authors.initials: MA
authors.lastname: Taylor
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology*; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D008875:Middle Aged; D008962:Models, Theoretical; D011379:Prognosis; D012008:Recurrence; D012306:Risk; D006113:United Kingdom
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