Is Regret Theory an alternative basis for estimating the value of healthcare interventions?

Smith, Richard

doi:10.1016/s0168-8510(96)90055-x

Cited by 50 publications

(19 citation statements)

References 26 publications

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“…Such findings raise an important question of which elasticity estimates should be of policy relevance. This reflection is congruent with the argument (Boeri et al 2013) that utility maximization framework is not the only driver of choices for the individuals in all situations of life and also supports the argument (Smith 1996) that utility theory-based measures of health do not necessarily reflect the true preferences of the individual.…”

Section: Discussionsupporting

confidence: 81%

A comparison of regret-based and utility-based discrete choice modelling – an empirical illustration with hospital bed choice

et al. 2018

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There is some concern that the unobserved preference heterogeneity in random utility maximization theory-based discrete choice experiment modelling is an important source of error variability. The randomness in utility is often interpreted as interpersonal preference heterogeneity but it can also be intrapersonal random variation in preferences. We compare utility maximization and regret minimization-based choice models' sensitivity to individual heterogeneity, examine differences between two consecrated models and validate with empirical illustrations. We use frequency of category (public, semi-private, and private) of bed chosen from Swiss cross-sectional datasets (2007)(2008)(2009)(2010)(2011)(2012) to compare two approaches -utility maximization and regret minimization by applying multinomial logit (MNL) models in regard to the variances in utility (regret) function, goodness-of-fit and predicted marginal effects (pseudo-elasticity) of additional payment. We find parameters with the same sign and estimates with almost same order of magnitude in both the approaches. The statistical significance of attribute effects is consistent in all variants of utility -based MNL models while effects of different attributes are significant only in heteroskedastic extreme value (HEV) variant of regret-based MNL models. This empirical illustration suggests that HEV variant of regret-based models perform better in capturing attribute effects in choice behaviour. KEYWORDSBehavioural paradigm; choice modelling; hospital bed choice; pseudo-elasticity and random regret JEL CLASSIFICATION C25; D12

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Section: Discussionsupporting

confidence: 81%

A comparison of regret-based and utility-based discrete choice modelling – an empirical illustration with hospital bed choice

et al. 2018

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“…Smith (1996) explored the concept of regret in healthcare decision making from the patient's viewpoint, arguing that valuation techniques based on the utility theory, such as QALYs, will not necessarily reflect the true preferences of the individual. Ziarnowski et al (2009) found that anticipated regret played an important role in the decision to vaccinate adolescent girls against HPV.…”

Section: These Studies Base Their Analysis On the Linear-in-parametermentioning

confidence: 99%

“…Since the first applications of DCE in health economics to value patient experiences (Ryan and Hughes, 1997;Ryan, 1999), this technique has become widely used to investigate a wide range of policy questions (Ryan et al, 2008 Evidence from previous studies suggests that regret can be an important factor in both medical decision making (Smith, 1996;Djulbegovic et al 1999 andSorum et al 2004) and personal healthcare decisions (Brehaut et al, 2003 andZiarnowski et al, 2009), particularly when the outcome of the choice may be unfavourable. Smith (1996) explored the concept of regret in healthcare decision making from the patient's viewpoint, arguing that valuation techniques based on the utility theory, such as QALYs, will not necessarily reflect the true preferences of the individual. Ziarnowski et al (2009) found that anticipated regret played an important role in the decision to vaccinate adolescent girls against HPV.…”

Section: Introductionmentioning

confidence: 99%

The role of regret minimisation in lifestyle choices affecting the risk of coronary heart disease

Boeri

Longo

Grisolía

et al. 2013

Journal of Health Economics

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Research Highlights: This paper introduces the discrete choice paradigm of Random Regret Minimization (RRM) to the field of health economics The combined use of RRM and Random Utility Maximization (RUM) models provide useful behavioural insights on choice Whilst the RUM is suitable for calculating welfare estimates, the RRM highlights how anticipated regret affects choices We find that the choices of overweight or obese respondents and smokers are more likely to conform to the RUM approach We also find that the choices of people in good health and with higher education are more likely to reflect the RRM approach AbstractPage 2 of 31 A c c e p t e d M a n u s c r i p t This paper introduces the discrete choice model-paradigm of Random Regret Minimization (RRM) to the field of health economics. The RRM is a regret-based model that explores a driver of choice different from the traditional utility-based Random Utility Maximization (RUM). The RRM approach is based on the idea that, when choosing, individuals aim to minimize their regret -regret being defined as what one experiences when a non-chosen alternative in a choice set performs better than a chosen one in relation to one or more attributes. Analysing data from a discrete choice experiment on diet, physical activity and risk of a fatal heart attack in the next ten years administered to a sample of the Northern Ireland population, we find that the combined use of RUM and RRM models offer additional information, providing useful behavioural insights for better informed policy appraisal.Keywords: Random Regret Minimization; Random Utility Maximization; dietary choices; physical activity; coronary heart disease risk; behavioural economics. Page 3 of 31A c c e p t e d M a n u s c r i p t IntroductionDiscrete choice experiments (DCE) are a survey-based technique used to investigate the trade-offs that people are prepared to make between different hypothetical goods or services, where respondents are shown alternative variants of the good or service described by a set of attributes and are asked to choose the most preferred one (Ryan and Hughes, 1997;Vick and Scott, 1998). Since the first applications of DCE in health economics to value patient experiences (Ryan and Hughes, 1997;Ryan, 1999), this technique has become widely used to investigate a wide range of policy questions (Ryan et al., 2008 Evidence from previous studies suggests that regret can be an important factor in both medical decision making (Smith, 1996;Djulbegovic et al. 1999 andSorum et al. 2004) and personal healthcare decisions (Brehaut et al., 2003 andZiarnowski et al., 2009), particularly when the outcome of the choice may be unfavourable. Smith (1996) explored the concept of regret in healthcare decision making from the patient's viewpoint, arguing that valuation techniques based on the utility theory, such as QALYs, will not necessarily reflect the true preferences of the individual. Ziarnowski et al. (2009) found that anticipated regret played an important role in the decision to vaccinate adol...

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“…A number of experimental studies have found that regret does influence decision making (Bleichrodt et al, 2007;Cooke et al, 2001;Janis and Mann, 1977;Sorum et al, 2004;Wolfson and Briggs, 2002;Zeelenberg, 1999;Zeelenberg et al, 1996). Furthermore, regret has been invoked to explain decision making that is inconsistent with expected utility theory in many domains outside the laboratory such as healthcare (Smith, 1996) or consumer decisions (Tsiros and Mittal, 2000). Empirical studies have shown that regret can explain empirical patterns in insurance purchases (Braun and Muermann, 2004), where it can account for a preference for low deductibles, financial investment (Dodonova and Khoroshilov, 2005;Muermann et al, 2006) in which it can explain excess volatility and autocorrelation of asset returns, bidding in auctions (Filiz and Ozbay, 2006), in which it can explain bidding higher than risk-neutral Nash equilibrium levels in first price auctions, and the timing of purchase decisions (Cooke et al, 2001), where it captures delays in making purchases.…”

Section: Introductionmentioning

confidence: 99%

Neurobiological regret and rejoice functions for aversive outcomes

et al. 2008

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A decision maker may experience regret when a choice he makes results in a more adverse outcome than a different choice would have yielded. Analogously, he may experience rejoice when his choice resulted in better outcomes. We used fMRI to investigate the neural correlates of regret and rejoice where payoffs are in terms of a non-monetary medium. Incentives were created using painful outcomes in the form of mild electrical shocks to the foot and the possibility of avoiding them. We hypothesized that the neural response to a painful outcome resulting from an individual's choice would also reflect the degree of regret as measured by the likelihood that alternative choices would have yielded the same adverse outcome. Similarly, when an individual avoids a potential shock, he would experience a degree of rejoice that correlates with the probability he had of receiving the shock. For example, winning a bet when winning was unlikely, even if the outcome is the same, evokes more rejoice than winning when it was highly probable. Our results suggest that activation of a cortical network, consisting of the medial orbitofrontal cortex, left superior frontal cortex, right angular gyrus, and left thalamus, correlates with the degree of regret. A different network, including the rostral anterior cingulate, left hippocampus, left ventral striatum, and brainstem/midbrain correlated with rejoice. The right inferior orbitofrontal cortex, pre-supplementary motor area, anterior cingulate, and posterior cingulate showed similar patterns of activation with both regret and rejoice, suggesting that these regions may be associated with surprise from the realization of relatively unlikely events. Our results suggest that distinct, but overlapping networks are involved in the experiences of regret and rejoice.

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Is Regret Theory an alternative basis for estimating the value of healthcare interventions?

Cited by 50 publications

References 26 publications

A comparison of regret-based and utility-based discrete choice modelling – an empirical illustration with hospital bed choice

A comparison of regret-based and utility-based discrete choice modelling – an empirical illustration with hospital bed choice

The role of regret minimisation in lifestyle choices affecting the risk of coronary heart disease

Neurobiological regret and rejoice functions for aversive outcomes

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