title: Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example.
abstract: BACKGROUND
  Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches.
  
  
  RESULTS
  Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived.
  
  
  CONCLUSIONS
  The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases.
authors.affiliation: Charité-Universitätsmedizin Berlin, Klinik für Gynäkologie mit Brustzentrum, Berlin, Germany.
authors.forename: Klaus-Jürgen
authors.initials: KJ
authors.lastname: Winzer
authors.affiliation: Universitätsklinikum Freiburg, Institut für Medizinische Biometrie und Statistik, Department für Medizinische Biometrie und Medizinische Informatik, Freiburg, Germany.|Universitätsklinikum Hamburg-Eppendorf, Institut für Medizinische Biometrie und Epidemiologie, Hamburg, Germany.
authors.forename: Anika
authors.initials: A
authors.lastname: Buchholz
authors.affiliation: Universitätsklinikum Freiburg, Institut für Medizinische Biometrie und Statistik, Department für Medizinische Biometrie und Medizinische Informatik, Freiburg, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Schumacher
authors.affiliation: Universitätsklinikum Freiburg, Institut für Medizinische Biometrie und Statistik, Department für Medizinische Biometrie und Medizinische Informatik, Freiburg, Germany.
authors.forename: Willi
authors.initials: W
authors.lastname: Sauerbrei
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism; D001943:Breast Neoplasms / Q000145:classification / Q000401:mortality / Q000473:pathology*; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008207:Lymphatic Metastasis; D015999:Multivariate Analysis; D060787:Neoplasm Grading; D011379:Prognosis; D016016:Proportional Hazards Models; D058996:Quality Improvement; D011956:Receptors, Cell Surface / Q000378:metabolism; D012015:Reference Standards; D012720:Severity of Illness Index; D047368:Tumor Burden
chemical_list: D014408:Biomarkers, Tumor; D011956:Receptors, Cell Surface