Comprehensive decision-analytic model and Bayesian value-of-information analysis: pentoxifylline in the treatment of chronic venous leg ulcers

Abstract

Objective: To conduct a Bayesian value-of-information analysis of the cost effectiveness of pentoxifylline (vs placebo) as an adjunct to compression for venous leg ulcers.

Methods: A probabilistic Markov model was developed to estimate mean clinical benefits and costs associated with oral pentoxifylline (400mg three times daily) and placebo. Clinical data were obtained from a systematic review and synthesised using Bayesian methods. The decision uncertainty associated with the adoption of pentoxifylline as well as the maximum value associated with further research were estimated before and after the completion of the largest 'definitive' treatment trial. Resource use was obtained from a UK national audit and unit costs applied (pounds, 2004 values).

Results: The prior and posterior analyses suggest that pentoxifylline is a dominant therapy versus placebo. In the prior analysis, patients in the pentoxifylline group healed an average of 8.28 weeks quicker than patients in the placebo group (95% credibility interval [CI] 1.89, 14.56), had a 0.02 gain in QALYs (95% CI -0.12, 0.17) and an average reduction in cost of 153.4 pounds (95% CI -53.11, 354.9). Estimates of the uncertainty surrounding the cost effectiveness of pentoxifylline and the value of perfect information in both analyses did not suggest further research was justified. In the prior analysis, for willingness-to-pay values of 0 pounds, 100 pounds and 500 pounds per QALY gained, the estimated values of perfect information were 128,200 pounds, 127,100 pounds and 126,700 pounds, respectively. Incorporation of the information from the largest randomised controlled trial on pentoxifylline did improve the estimate of the clinical effect associated with this drug; however, the variation was not large enough to reverse either the decision regarding the dominance of pentoxifylline or the maximum value associated with further research.

Conclusion: Bayesian value-of-information analysis represents a valuable tool for healthcare decision making. Had the results from this analysis been available before the largest trial was funded, a more efficient allocation of research and development resources could have been made.