Med Decis Making - Eliciting benefit-risk preferences and probability-weighted utility using choice-format conjoint analysis.

Tópicos

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Resumo

This study applies conjoint analysis to estimate health-related benefit-risk tradeoffs in a non-expected-utility framework. We demonstrate how this method can be used to test for and estimate nonlinear weighting of adverse-event probabilities and we explore the implications of nonlinear weighting on maximum acceptable risk (MAR) measures of risk tolerance. We obtained preference data from 570 Crohn's disease patients using a web-enabled conjoint survey. Respondents were presented with choice tasks involving treatment options that involve different efficacy benefits and different mortality risks for 3 possible side effects. Using conditional logit maximum likelihood estimation, we estimate preference parameters using 3 models that allow for nonlinear preference weighting of risks--a categorical model, a simple-weighting model, and a rank dependent utility (RDU) model. For the second 2 models we specify and jointly estimate 1- and 2-parameter probability weighting functions. Although the 2-parameter functions are more flexible, estimation of the 1-parameter functions generally performed better. Despite well-known conceptual limitations, the simple-weighting model allows us to estimate weighting function parameters that vary across 3 risk types, and we find some evidence of statistically significant differences across risks. The parameter estimates from RDU model with the single-parameter weighting function provide the most robust estimates of MAR. For an improvement in Crohn's symptom severity from moderate and mild, we estimate maximum 10-year mortality risk tolerances ranging from 2.6% to 7.1%. Our results provide further the evidence that quantitative benefit-risk analysis used to evaluate medical interventions should account explicitly for the nonlinear probability weighting of preferences.

Resumo Limpo

studi appli conjoint analysi estim healthrel benefitrisk tradeoff nonexpectedutil framework demonstr method can use test estim nonlinear weight adverseev probabl explor implic nonlinear weight maximum accept risk mar measur risk toler obtain prefer data crohn diseas patient use weben conjoint survey respond present choic task involv treatment option involv differ efficaci benefit differ mortal risk possibl side effect use condit logit maximum likelihood estim estim prefer paramet use model allow nonlinear prefer weight risksa categor model simpleweight model rank depend util rdu model second model specifi joint estim paramet probabl weight function although paramet function flexibl estim paramet function general perform better despit wellknown conceptu limit simpleweight model allow us estim weight function paramet vari across risk type find evid statist signific differ across risk paramet estim rdu model singleparamet weight function provid robust estim mar improv crohn symptom sever moder mild estim maximum year mortal risk toler rang result provid evid quantit benefitrisk analysi use evalu medic intervent account explicit nonlinear probabl weight prefer

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