Location of Repository

Cost-effectiveness in clinical trials : using multiple imputation to deal with incomplete cost data

By Andrea Burton, Lucina Jane Billingham and Stirling Bryan


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 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 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 multiple imputation, the results indicated that chemotherapy had a high probability of being cost-effective for a societal willingness to pay over £20,000 per life-year gained. This was in stark contrast with the complete case analysis, which suggested that chemotherapy was not a cost-effective use of resources at any reasonable level of willingness to pay for a life-year. Limitations: Our findings are based on a relatively small retrospective study with all events observed. Conclusion: In conclusion, cost-effectiveness analysis of the complete cases only may give biased results, and therefore, in situations where there are missing costs, multiple imputation is recommended

Topics: R1, QA
Publisher: Sage
Year: 2007
OAI identifier: oai:wrap.warwick.ac.uk:92

Suggested articles



  1. Analysis of cost data in randomized trials: an application of the non-parametric bootstrap. doi
  2. Analysis of incomplete multivariate data. Chapman and doi
  3. (2005). Available at http://methodology.psu.edu/publications/fcsmfinal.pdf.
  4. Bayesian estimation of cost effectiveness from censored data. doi
  5. Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. doi
  6. (2005). Core Team doi
  7. (2006). Dealing with missing data. Res Lett Inf Math Sci 2002;3:153-160. Available at http://www.massey.ac.nz/~wwiims/research/letters/volume3number1. Accessed
  8. Definition, interpretation and calculation of costeffectiveness acceptability curves. doi
  9. Handling uncertainty when performing economic evaluations of healthcare interventions. doi
  10. (2005). How sensitive are cost-effectiveness analyses to choice of parametric distributions? Med Decis Making doi
  11. (1999). ifosfamide and cisplatin in unresectable non-small-cell lung cancer: effects on survival and quality of life.
  12. Missing data, imputation and the bootstrap. doi
  13. Missing data: Our view of the state of the art. doi
  14. Missing… Presumed at random: Cost analysis of incomplete data. doi
  15. Modelling and imputation of semicontinuous survey variables. The Methodology Center,
  16. Multiple imputation for incomplete data with semicontinuous variables. doi
  17. (1987). Multiple imputation for nonresponse in surveys. doi
  18. (1999). Multiple imputation of missing blood pressure covariates in survival analysis. doi
  19. (2005). NORM Version 2.02 for windows: Multiple imputation of incomplete multivariate data under a normal model. Available at http://www.stat.psu.edu/~jls/misoftwa.html.
  20. Patterns and costs of care in advanced Non-small cell lung cancer in a trial of chemotherapy versus supportive care. doi
  21. Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost-effectiveness analysis. doi
  22. (1987). Statistical analysis with missing data. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.