title: Mortality versus survival graphs: improving temporal consistency in perceptions of treatment effectiveness.
abstract: OBJECTIVE
  Previous research has demonstrated that people perceive treatments as less effective when survival graphs show fewer years of data versus more data. We tested whether using mortality graphs would reduce this temporal inconsistency bias.
  
  
  METHODS
  A demographically diverse sample of 1461 Internet users read about a hypothetical disease and then were randomized to view either survival or mortality graphs that showed either 5 years of data or 15 years of treatment outcomes data. Participants identified the most effective treatment, provided ratings comparing the effectiveness of two treatments, and answered comprehension questions.
  
  
  RESULTS
  Treatment effectiveness ratings varied significantly between respondents seeing the 5 year and 15 year survival graphs even though the relative risk reduction was the same in both cases. This variation was significantly reduced in the mortality graph conditions. Responses on comprehension measures were mixed: viewers of mortality graphs were less able to identify which treatment was more effective but better able to correctly report individual data points.
  
  
  CONCLUSIONS
  Perceptions of treatment effectiveness appear more temporally consistent with mortality graphs than with survival graphs.
  
  
  CONCLUSIONS
  All line-based risk graphics (whether framed in survival or mortality terms) should highlight duration information to facilitate improved comprehension of treatment effectiveness.
authors.affiliation: bzikmund@umich.eduVA Health Services Research & Development Center for Practice Management and Outcomes Research, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States.
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Zikmund-Fisher
authors.forename: Angela
authors.initials: A
authors.lastname: Fagerlin
authors.forename: Peter A
authors.initials: PA
authors.lastname: Ubel
mesh_terms: D000328:Adult; D000368:Aged; D000704:Analysis of Variance; D001294:Attitude to Health; D015982:Bias; D016009:Chi-Square Distribution; D032882:Comprehension; D003196:Computer Graphics / Q000592:standards*; D003194:Computer-Assisted Instruction; D003627:Data Interpretation, Statistical; D005163:Factor Analysis, Statistical; D006801:Humans; D020407:Internet; D008136:Longevity; D008875:Middle Aged; D009026:Mortality; D010353:Patient Education as Topic; D018570:Risk Assessment; D040242:Risk Reduction Behavior; D011795:Surveys and Questionnaires; D015996:Survival Rate; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States