title: Cost-effectiveness in clinical trials: using multiple imputation to deal with incomplete cost data.
abstract: BACKGROUND
  Cost-effectiveness has become an important outcome in many clinical trials and has resulted in the collection of resource use data and the calculation of costs for individual patients. A specific example is a Cancer Research UK phase III trial comparing chemotherapy (CT) against standard palliative care in patients with advanced non-small cell lung cancer. Resource usage from trial entry until death were collected and costs obtained on a subset of 115 trial patients. For some patients, however, the unavailability of medical notes resulted in some cost components, and hence total cost, being missing. The 82 patients with complete data were not representative of all trial patients in terms of effectiveness and thus it was necessary to address the missing data problem.
  
  
  METHODS
  Multiple imputation (MI) was used to impute values for the unobserved individual cost components, allowing total cost to be calculated and cost-effectiveness carried out for all patients in the cost sub-study. The results are compared with those from a complete case analysis.
  
  
  RESULTS
  After MI, the results indicated that CT had a high probability of being cost-effective for a societal willingness to pay over 20,000 Pounds per life-year gained. This was in stark contrast with the complete case analysis, which suggested that CT was not a cost-effective use of resources at any reasonable level of willingness to pay for a life-year.
  
  
  CONCLUSIONS
  Our findings are based on a relatively small retrospective study with all events observed.
  
  
  CONCLUSIONS
  In conclusion, cost-effectiveness analysis of the complete cases only may give biased results, and therefore, in situations where there are missing costs, MI is recommended.
authors: Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, UK. andrea.burton@warwick.ac.uk.
authors: Andrea
authors: A
authors: Burton
authors: Lucinda Jane
authors: LJ
authors: Billingham
authors: Stirling
authors: S
authors: Bryan
mesh_terms: D015982:Bias; D002289:Carcinoma, Non-Small-Cell Lung / Q000188:drug therapy; D003362:Cost-Benefit Analysis; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy; D008297:Male; D016032:Randomized Controlled Trials as Topic / Q000191:economics*