LAUSR

BACKGROUND Selecting the best treatment for life-critical conditions via a shared decision making approach is a uniquely important challenge. Besides data from the healthcare physicians, other data that need to be considered are the personal values and perceptions of the patient. Usually, these data come in the form of health-state utility values. They are subjective and often times are elicited from the patient under emotional and stressful conditions. This paper examines an approach for selecting the best treatment under a life-critical shared decision making (SDM) framework. METHODS Health-state utility values are used in practice to quantify what is known as quality-adjusted life years (QALYs) and quality-adjusted life expectancy (QALE). The QALEs from different treatments are used to select the best treatment. This paper describes methods for determining QALEs under a range of scenarios defined by the way some key assumptions on the health-state utility values are satisfied. Approaches for comparing different treatments are described along with some counter-intuitive results. These approaches are based on some optimization formulations. The proposed approaches are demonstrated in terms of a real example taken from the literature. RESULTS Having results that are robust under a spectrum of different scenarios can provide more confidence that the most suitable treatment has been selected in a given case. On the other hand, having non-robust results can be useful information too as they may provide evidence that a more thorough assessment of the benefits and harms of the treatments may be needed to select a treatment with higher confidence. Finally, this study demonstrates that under certain mathematical conditions among the data it is possible to decide which treatment is better among two treatments without having to use health-state utility values. CONCLUSION The significance of this study is that it provides valuable and actionable insights for the important question of how health-state utilities can be used in treatment selection.