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Maximum Acceptable Risk Estimation Based on a Discrete Choice Experiment and a Probabilistic Threshold Technique.

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The discrete choice experiment (DCE) and probabilistic threshold technique (PTT) showed overlapping maximum acceptable risk estimates for serious side effects but differed for mild ones. DCE tasks were rated as easier to understand, suggesting its suitability for complex risk assessments.

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Area of Science:

  • Health Economics
  • Decision Science
  • Risk Perception

Background:

  • Estimating maximum acceptable risk is crucial for informed decision-making in healthcare.
  • Discrete Choice Experiments (DCE) and Probabilistic Threshold Techniques (PTT) are methods used to elicit these risk preferences.
  • Empirical comparison of these methods is needed to understand their relative strengths and weaknesses.

Purpose of the Study:

  • To empirically compare maximum acceptable risk estimates derived from a DCE and a PTT.
  • To assess the perceived complexity and preference heterogeneity associated with each method.

Main Methods:

  • A UK general public sample (n=982) completed online surveys with both DCE and PTT components.
  • DCE utilized a Bayesian D-efficient design with six attributes and 15 choice tasks per block.
  • PTT used identical risk and benefit attributes; panel mixed-logit and interval regression models were employed respectively.

Main Results:

  • Confidence intervals for maximum acceptable risk overlapped for serious infection and serious side effects, but not for mild side effects.
  • Respondents found DCE tasks significantly easier to understand than PTT tasks (7-percentage point difference, p < 0.05).
  • Maximum acceptable risk for mild side effects was notably lower with PTT.

Conclusions:

  • DCE and PTT provide comparable risk estimates for serious adverse events but may differ for less severe ones.
  • The DCE appears better suited for studies involving multiple risk attributes of varying severity due to its perceived ease of use.
  • PTT may be more appropriate for investigating heterogeneity in risk estimates or focusing on specific serious adverse events.