The Discrepancy between Risky and Riskless Utilities

Abstract

Utilities differ according to whether they are derived from risky (gamble) and riskless (visual analog scale, time-tradeoff) assessment methods. The discrepancies are usually explained by assuming that the utilities elicited by risky methods incorporate attitudes towards risk, whereas riskless utilities do not. In (cumulative) prospect theory, risk attitude is conceived as consisting of two components: a decision-weight function (attentiveness to changes in, or sensitivity towards, chance) and a utility function (sensitivity towards outcomes). The authors' data suggest that a framing effect is a hitherto unrecognized and important factor in causing discrepancies between risky and riskless utilities. They collected risky evaluations with the gamble method, and riskless evaluations with difference measurement. Risky utilities were derived using expected-utility theory and prospect theory. With the latter approach, sensitivity towards outcomes and sensitivity towards chance are modeled separately. When the hypothesis that risky utilities from prospect theory coincide with riskless utilities was tested, it was rejected (n = 8, F(1,7) = 132, p = 0.000), suggesting that a correction for sensitivity towards chance is not sufficient to resolve the difference between risky and riskless utilities. Next, it was assumed that different gain/loss frames are induced by risky and riskless elicitation methods. Indeed, identical utility functions were obtained when the gain/loss frames were made identical across methods (n = 7), suggesting that framing was operative. The results suggest that risky and riskless utilities are identical after corrections for sensitivity towards chance and framing.

Deriving Time Discounting Correction Factors for TTO Tariffs

“…Loss aversion occurs if individuals adopt a reference point and consider outcomes as deviations from this reference point. Higher outcomes are seen as gains, and lower outcomes as losses, with losses looming larger than gains, and, hence, receiving more weight than commensurate gains (Kahneman and Tversky, 1979;McNeil et al, 1982;Tversky and Kahneman, 1991;Tversky and Kahneman, 1992;Stalmeier and Bezembinder, 1999;Bleichrodt and Pinto, 2002). The effect of loss aversion on TTO scores depends on the elicitation procedure.…”

“…These biases are consequences of EU often being descriptively falsified (Llewellyn-Thomas et al, 1982;Rutten-van Molken et al, 1995). First, people tend to attach nonlinear decision weights to probabilities instead of handling them linearly (Wakker and Stiggelbout, 1995;Bleichrodt et al, 1999;Stalmeier and Bezembinder, 1999;Bleichrodt and Pinto, 2000;Bleichrodt, 2001). Second, loss aversion causes individuals to have a reference point (e.g., the certain option) and may consider the worst outcome of the gamble a loss, which they give more weight.…”

Palliative chemotherapy or best supportive care? A prospective study explaining patients' treatment preference and choice

“…In contrast to the lack of studies on treatment choice, several studies have been performed on the nature and background of treatment preference (Cassileth et al, 1980;McNeil et al, 1982;Llewellyn-Thomas et al, 1995;Flood et al, 1996;Brundage et al, 1997;Mazur and Hickam, 1997;Lindley et al, 1998;Silvestri et al, 1998;Stalmeier and Bezembinder, 1999;Stiggelbout and de Haes, 2001). In the literature, the patient's preference for treatment is seen and used as an indication for their actual treatment choice.…”

On the Role of Framing Effects in Assessment of Health-related Utilities

“…Entwistle et al 4 have begun to clarify this issue by classifying potential effects of interventions on decision making and health outcomes depending on the model of patient involvement (1, shared decision making; 2, individual informed choice; 3, professional as agent for the patient; 4, promotion of rational decision making; or 5, promotion of a particular choice). For example, one indicator of efficacy using model 5 may be patient compliance with a recommended option, whereas in model 4, an intervention that increases the selection of options that maximize the patients' expected utility may be judged successful.…”

“…Entwistle et al 4 have begun to clarify this issue by classifying potential effects of interventions on decision making and health outcomes depending on the model of patient involvement (1, shared decision making; 2, individual informed choice; 3, professional as agent for the patient; 4, promotion of rational decision making; or 5, promotion of a particular choice). For example, one indicator of efficacy using model 5 may be patient compliance with a recommended option, whereas in model 4, an intervention that increases the selection of options that maximize the patients' expected utility may be judged successful. Entwistle et al also acknowledge5 that promoting "evidence-informed patient choice" is seen by some as the desired end, whereas others see it as a means to other desirable outcomes, such as greater clinical effectiveness, health gain, individually appropriate utilization, reduced expenditures on inappropriate interventions, reduced litigation, etc.…”