Modeling therapeutic benefit in the midst of uncertainty

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Abstract
Randomized, double-blind, controlled clinical trials are the gold standard for medical therapy. When a disease is rare or slowly progressive over many years, such a trial may not be feasible. Decision analysis provides a bridge between current studies with short-term surrogate markers and a large, longitudinal clinical trial. With decision analysis, results of current studies can be summarized and the outcome of a long-term study projected under explicit assumptions. Chronic hepatitis C is a disease that is slowly progressive, and the requirements of a longitudinal clinical study could be prohibitive. Therefore, we review the basic steps of decision analysis, apply these steps to two recent decision analyses evaluating the use of interferon-α2b (IFN) in the treatment of chronic hepatitis C, and discuss possible implications of decision modeling for this disease. The estimated marginal cost per year of life gained from IFN therapy ranged from approximately $3000 to $55,000 in these two studies. The wide range is based on different estimates of treatment costs and disease progression. This analysis has identified gaps in the current knowledge regarding the natural progression of hepatitis C and has established criteria to evaluate new developments and their impact on chronic hepatitis C.