Decision making for preventive interventions in asymptomatic patients, such as the treatment of incidental intracranial aneurysms, is eminently uncertain and at risk of over-treatment. One approach suggests that the weighing of the natural risk of the disease against the risk of intervention should be replaced by a comparison of outcomes measured as expected quality-adjusted life-years survival.

We review the problems of over-diagnosis and over-treatment and how prognostic studies can help address the clinical uncertainty. We examine and compare the assumptions that underlie the mathematical transformations that are involved in the so-called outcome-based approach with the risk-based approach when they are both derived from observational data. Finally, we propose a more pragmatic approach.

Both risk-based and outcome-based models depend on two strong assumptions: exchangeability of patients selected to be observed and patients selected to be treated (in other words ignorability of treatment assignment), and ii) dominance of time-to-event data (the only thing pertinent for decision making is the time to the first event in the patient’s history). The outcome-based approach needs an additional assumption: fatality (once a patient suffers a poor outcome from an event, recovery is impossible). These three theoretical assumptions are rarely verified in practice.

Clinical decision-making based on observational data relies on unrealistic assumptions. Clinical practice should instead be guided by conducting pragmatic clinical trials.