Abstract
A number of recently published studies on the cost-effectiveness of cholesterol-lowering therapy use data from the Framingham Study to model the effect of cholesterol lowering on coronary heart disease risk. However, the risk estimates from the Framingham Study underestimate the association between coronary heart disease risk and serum cholesterol level because they do not account for intraindividual biological variation and analytical variation in cholesterol measurement. Cost-effectiveness studies that use these risk estimates are therefore likely to overestimate the cost per year of life saved of cholesterol-lowering interventions. We have developed an algorithm that can be used to improve current estimates of the cost-effectiveness of cholesterol-lowering therapy. Our results show that adjusting for intraindividual biological variation and analytical variation lowers the cost per year of life saved by 17 to 29%, depending on sex, pretreatment cholesterol level, and age at initiation of therapy.
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