Method for Calculating the Simultaneous Maximum Acceptable Risk Threshold (SMART) from Discrete-Choice Experiment Benefit-Risk Studies

Background Medical decisions require weighing expected benefits of treatment against multiple adverse outcomes under uncertainty (i.e., risks) that must be accepted as a bundle. However, conventional maximum acceptable risk (MAR) estimates derived from discrete-choice experiment benefit-risk studies evaluate the acceptance of individual risks, assuming other risks are fixed, potentially leading decision makers to misinterpret levels of risk acceptance. Design Using simulations and a published discrete-choice experiment, we demonstrate a method for identifying multidimensional risk-tolerance measures given a treatment level of benefit. Results Simultaneous Maximum Acceptable Risk Thresholds (SMART) represents combinations of risks that would be jointly accepted in exchange for specific treatment benefits. The framework shows how the expectation of utility associated with treatments that involve multiple risks are related even when preferences for potential adverse events are independent. We find that the form of the marginal effects of adverse-event probabilities on the expected utility of treatment determines the magnitude of differences between SMART and conventional single-outcome MAR estimates. Limitations Preferences for potential adverse events not considered in a study or preferences for adverse-event attributes held constant in risk-tolerance calculations may affect estimated risk tolerance. Further research is needed to understand the right balance between realistically reflecting clinical treatments with many potential adverse events and the cognitive burden of evaluating risk-risk tradeoffs in research and in practice. Conclusions and Implications SMART analysis should be considered in preference studies evaluating the joint acceptance of multiple potential adverse events.

Automated summary

This study introduces a method for evaluating multidimensional risk tolerance, which can be used to assess the joint acceptance of multiple potential adverse events in medical decision-making. The research findings suggest that conventional risk assessment methods may lead to misinterpretations of risk acceptance levels.