title: The value of source data verification in a cancer clinical trial.
abstract: BACKGROUND
  Source data verification (SDV) is a resource intensive method of quality assurance frequently used in clinical trials. There is no empirical evidence to suggest that SDV would impact on comparative treatment effect results from a clinical trial.
  
  
  METHODS
  Data discrepancies and comparative treatment effects obtained following 100% SDV were compared to those based on data without SDV. Overall survival (OS) and Progression-free survival (PFS) were compared using Kaplan-Meier curves, log-rank tests and Cox models. Tumour response classifications and comparative treatment Odds Ratios (ORs) for the outcome objective response rate, and number of Serious Adverse Events (SAEs) were compared. OS estimates based on SDV data were compared against estimates obtained from centrally monitored data.
  
  
  RESULTS
  Data discrepancies were identified between different monitoring procedures for the majority of variables examined, with some variation in discrepancy rates. There were no systematic patterns to discrepancies and their impact was negligible on OS, the primary outcome of the trial (HR (95% CI): 1.18(0.99 to 1.41), p = 0.064 with 100% SDV; 1.18(0.99 to 1.42), p = 0.068 without SDV; 1.18(0.99 to 1.40), p = 0.073 with central monitoring). Results were similar for PFS. More extreme discrepancies were found for the subjective outcome overall objective response (OR (95% CI): 1.67(1.04 to 2.68), p = 0.03 with 100% SDV; 2.45(1.49 to 4.04), p = 0.0003 without any SDV) which was mostly due to differing CT scans.
  
  
  CONCLUSIONS
  Quality assurance methods used in clinical trials should be informed by empirical evidence. In this empirical comparison, SDV was expensive and identified random errors that made little impact on results and clinical conclusions of the trial. Central monitoring using an external data source was a more efficient approach for the primary outcome of OS. For the subjective outcome objective response, an independent blinded review committee and tracking system to monitor missing scan data could be more efficient than SDV.
authors.affiliation: Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom. cat1@liv.ac.uk
authors.forename: Catrin
authors.initials: C
authors.lastname: Tudur Smith
authors.forename: Deborah D
authors.initials: DD
authors.lastname: Stocken
authors.forename: Janet
authors.initials: J
authors.lastname: Dunn
authors.forename: Trevor
authors.initials: T
authors.lastname: Cox
authors.forename: Paula
authors.initials: P
authors.lastname: Ghaneh
authors.forename: David
authors.initials: D
authors.lastname: Cunningham
authors.forename: John P
authors.initials: JP
authors.lastname: Neoptolemos
mesh_terms: D002986:Clinical Trials as Topic / Q000009:adverse effects / Q000191:economics; D003365:Costs and Cost Analysis; D018572:Disease-Free Survival; D006801:Humans; D053208:Kaplan-Meier Estimate; D009369:Neoplasms / Q000000981:diagnostic imaging / Q000191:economics / Q000628:therapy*; D011785:Quality Assurance, Health Care / Q000379:methods*; D015203:Reproducibility of Results; D013223:Statistics as Topic; D014057:Tomography, X-Ray Computed; D016896:Treatment Outcome