Statins and the Classic Decision Analysis
Treat, Test, or Neither?
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See Article by Jarmul et al
Anyone who has taken or taught an introductory decision analysis course is likely familiar with the classic treat all versus test versus neither problem. The basic problem is elegant enough to be broken down into 3 main components: (1) the probability of developing disease (or having the disease, for patients with suspected disease), (2) the consequences of acting with or without the test results (treating true and false positives, not treating true, and false negatives), and (3) the tests risks (if any) and its diagnostic performance (quantified as a disease association measure or sensitivity and specificity).1 Advancements in disease simulation modeling and cost-effectiveness analysis methods have broadened the scope of decision problems that can be evaluated using this framework, including the question of what the optimal threshold of predicted risk should be for the initiation of statin treatment for primary prevention of atherosclerotic cardiovascular disease (ASCVD). In their article, Jarmul et al2 used sophisticated cost-effectiveness modeling methods to ask: should physicians initiate statin treatment for all asymptomatic individuals with low to intermediate ASCVD risk (treat all), base this decision on results from a cardiovascular genetic risk score (cGRS) (test), or do neither?
Using a cost-effectiveness threshold of $50 000/quality-adjusted life year, the authors found that cGRS testing was not a cost-effective strategy compared with treat all or treat none strategies under base case assumptions …