title: Interpreting trial results in light of conflicting evidence: a Bayesian analysis of adjuvant chemotherapy for non-small-cell lung cancer.
abstract: OBJECTIVE
  When successive randomized trials contradict prior evidence, clinicians may be unsure how to evaluate them: Does accumulating evidence warrant changing practice? An increasingly popular solution, Bayesian statistics quantitatively evaluate new results in context. This study provides a clinically relevant example of Bayesian methods.
  
  
  METHODS
  Three recent non-small-cell lung cancer adjuvant chemotherapy trials were evaluated in light of prior conflicting data. Results were used from International Adjuvant Lung Trial (IALT), JBR.10, and Adjuvant Navelbine International Trialist Association (ANITA). Prior evidence was sequentially updated to calculate the probability of each survival benefit level (overall and by stage) and variance. Sensitivity analysis was performed using expert opinion and uninformed estimates of survival benefit prior probability.
  
  
  RESULTS
  The probability of a 4% survival benefit increased from 33% before IALT to 64% after IALT. After sequential updating with JBR.10 and ANITA, this probability was 82% (hazard ratio = 0.84; 95% CI, 0.77 to 0.91). IALT produced the largest decrease in variance (61%) and decreased the chance of survival decrement to 0%. Sensitivity analysis did not support a survival benefit after IALT. However, sequential updating substantiated a 4% survival benefit and, for stage II and III, more than 90% probability of a 6% benefit and 50% probability of a 12% benefit.
  
  
  CONCLUSIONS
  When evaluated in context with prior data, IALT did not support a 4% survival benefit. However, sequential updating with JBR.10 and ANITA did. A model for future assessments, this study demonstrates the unique ability of Bayesian analysis to evaluate results that contradict prior evidence.
authors.affiliation: Department of Medicine, Division of Hematology and Oncology, Beth Israel Deaconess Hospital, Harvard Medical School, Boston, MA 02215, USA. rmiksad@bidmc.harvard.edu
authors.forename: Rebecca A
authors.initials: RA
authors.lastname: Miksad
authors.forename: Mithat
authors.initials: M
authors.lastname: Gönen
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Lynch
authors.forename: Thomas G
authors.initials: TG
authors.lastname: Roberts
mesh_terms: D001499:Bayes Theorem; D002289:Carcinoma, Non-Small-Cell Lung / Q000188:drug therapy* / Q000401:mortality / Q000473:pathology; D017024:Chemotherapy, Adjuvant; D002986:Clinical Trials as Topic; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy* / Q000401:mortality / Q000473:pathology; D009367:Neoplasm Staging; D011336:Probability