BEST-WORST SCALING ALLOWS US TO QUANTIFY ATTITUDES AS WELL AS PREFERENCES; RESPONSE TIMES TELL US WHICH ARE "GUT" ATTITUDES WITH NO PREDICTIVE POWER
To understand whether supplementing Case 1 (Object Case) best-worst scaling (BWS) data to quantify attitudes towards end-of-life care with response time data produced similar results or whether attitudes like “all life is sacred” merely evoke “fast, gut” responses of the Kahneman type.
1186 respondents aged 55+ in Australia answered two online discrete choice experiments, which logged response times. One DCE was a simple “accept/reject treatment” response to each end-of-life clinical scenario from a full factorial in 16 (4x2x2). The other was a Case 1 BWS study in 13 choice sets to quantify degree of agreement with 13 attitudes towards end-of-life care spanning concepts including “pro-life”, “pro-quality of life” and “control over decision-making”. Traditional logit-based BWS models of the choice data were compared with hierarchical Bayesian implementation of the best-worst Linear Ballistic Accumulator (LBA) models (2013 and 2014) which conceptualise the random utility model as a “horse race” type psychological process.
Certain divergencies arose from the two models. (1) the “considered response” (I would prefer a course of treatment that focused on extending life as much as possible, even if that meant more pain and discomfort) and the “gut response” (all human life is sacred) are approximately equally disliked in the choice data. However, (2) when adding the response times the “gut response” is disliked far more. Three DCE segments were found, (1) the largest, close to two thirds, virtually always rejected treatment, (2) the second, close to one third, switched answers depending on the attribute levels on offer, (3) the smallest (7-9%) virtually always wanted treatment.
DCEs to elicit advance care plans involving complex clinical scenarios are difficult. Case 1 BWS studies that successfully predict preferences from more general attitudes would help uptake of advances care planning. Since the DCE showed that the vast majority of Australians do not want life extension, attitudes that help distinguish those one third of Australians with “it depends” preferences are more helpful in advance care planning than ones that simply induce strong disagreement - with little to no predictive ability of preferences. This study provides strong quantitative evidence supporting a priori hypotheses the authors had concerning which attitudes are likely to be helpful in predicting preferences.