title: Profile-specific survival estimates: making reports of clinical trials more patient-relevant.
abstract: BACKGROUND
  When considering treatment options, a physician needs to know the prognosis corresponding to the risk profile of the patient seeking treatment. Reports of clinical trials generally address treatment-specific survival probabilities only in the aggregate, i.e., for the typical patient, and often express the difference in survival as a hazard ratio. Such summaries do not provide treatment-specific survival probabilities (and thus the absolute difference in these probabilities) for patient profiles that are not near the typical of those in the trial. Despite the fact that Cox intended his hazard regression method to be used to produce such profile-specific survival estimates, and even showed how to calculate them, authors are either unaware that this is possible, or else choose not to report them.
  
  
  OBJECTIVE
  To illustrate how treatment- and profile-specific survival estimates are obtained from the Cox method, and can be displayed in a compact form.
  
  
  METHODS
  We derive treatment- and profile-specific survival probabilities from the estimated survival function for the ;reference' profile. Data from the Systolic Hypertension in the Elderly Program study serve as an illustration.
  
  
  RESULTS
  Two different formats, tabular and nomogram-based, allow the entire set of estimated treatment- and profile-specific survival probabilities to be reported.
  
  
  CONCLUSIONS
  Estimates are limited to the profiles within the covariate-space spanned by the trial, and depend on the correctness of the model.
  
  
  CONCLUSIONS
  Treatment- and profile-specific survival estimates are practice-relevant, almost never reported, estimable from the Cox model, and easy to report in a compact form.
authors: Department of Mathematics and Statistics, McGill University Montreal, Quebec, Canada.
authors: Marilyse
authors: M
authors: Julien
authors: James A
authors: JA
authors: Hanley
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000959:Antihypertensive Agents / Q000627:therapeutic use; D044383:Black People; D001794:Blood Pressure; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D006973:Hypertension / Q000188:drug therapy; D008297:Male; D008875:Middle Aged; D011336:Probability; D016016:Proportional Hazards Models; D012107:Research Design; D012306:Risk; D018570:Risk Assessment; D012737:Sex Factors; D020521:Stroke / Q000208:ethnology / Q000517:prevention & control; D016019:Survival Analysis; D044465:White People
chemical_list: D000959:Antihypertensive Agents