Purpose: To extend the methods developed by Phelps and Mushlin (MDM, 1988) and demonstrate the power of a ‘rapid’ cost-effectiveness analysis of new diagnostic tests compared to existing tests based on minimal information and without having to develop a full decision-analytic modelling framework, which is often complex, time consuming and may be an inefficient use of resources.
Method: Using a simplified decision-analytic approach to the complete pathway of care from diagnosis to subsequent treatment, the cost-effectiveness of the diagnostic test under consideration is expressed as a mathematical function of diagnostic accuracy, cost, burden, and the cost-effectiveness of treatment. This function only includes parameters likely to be available during the early stages of test development, and allows instantaneous estimation of cost-effectiveness, i.e. it does not require any simulation. Uncertainty in these parameters is accounted for by applying probabilistic sensitivity analysis. Using a clinical example, the cost-effectiveness of magnetic resonance angiography (MRA) compared with digital subtraction angiography (DSA) for the detection of new intracranial aneurysms is assessed in patients with previous subarachnoid hemorrhage.
Result: The simplified approach produced cost-effectiveness results in line with our previous and similar, but much more comprehensive, assessment of cost-effectiveness of MRA compared with DSA. The comprehensive assessment resulted in a net monetary benefit (NMB) of $1,910 (95%CI -1,809 to 5,565) and probabilities of effectiveness and cost-effectiveness of 98% and 87%, respectively, for a willingness-to-pay threshold of $50,000 per QALY. Our simplified approach returned a NMB of $1,779 (95%CI 1,170 to 2,477) with corresponding probabilities of effectiveness and cost-effectiveness of 100% and 98%, respectively. Hence, in our clinical example the simplified approach would provide sufficient information and a clear indication of the potential benefits of replacing DSA with MRA.
Conclusion: Given the increasing abundance of newly developed diagnostic tests a rapid approximation of the cost-effectiveness of new diagnostic tests compared with existing tests at minimal costs is highly valuable. The low-cost mathematical satisficing approach supports improved use of health care resources by indicating 1) which tests are promising and should be developed further, 2) which tests are not promising and could have their development discontinued, and 3) which tests require more rigorous and comprehensive economic evaluations to obtain improved estimates of cost-effectiveness but at a higher use of health care resources.