P19 Assessing Heterogeneity in MAR: Methods and Models Beyond DCE

Discrete choice experiments (DCEs) are robust stated-preference methods frequently used to estimate maximum acceptable risk (MAR) as a secondary outcome. However, DCEs provide sample-level estimates and explaining preference heterogeneity for MARs based on participant characteristics can be difficult. The study objective was compare the capability of DCE probabilistic threshold technique (PTT) identify among preventive rheumatoid arthritis (RA) treatment. Participants from 3 countries (United Kingdom (UK), Germany, Romania, n = 2959) completed PTT in random order. made choices between treatments that reduced chance developing RA but increased three risks (mild serious side effects, infection). For PTT, interval regressions estimated accounted age, education, numeracy, literacy, family history. DCE, parameters logit (RPL) models were calculate subgroups which identified PTT. Regarding these characteristics, statistically significantly different (p<0.05) at least one two countries. infection UK participants with low vs. high numeracy (p<0.05). Using no other combinations risks, or more by directly incorporating regression model. When attempting MARs, may partially overcome challenges stratified models, particularly if analyses such latent class analysis not feasible desirable. Further research is needed confirm findings this case studies explore method most accurately true underlying needed.