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Including Opt-Out Options in Discrete Choice Experiments: Issues to Consider
by
Campbell, Danny
, Erdem, Seda
in
Algorithms
/ Behavior
/ Choice Behavior
/ Experiments
/ Health Administration
/ Health Economics
/ Health Services Research
/ Humans
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Monte Carlo Method
/ Monte Carlo simulation
/ Patient Preference
/ Pharmacoeconomics and Health Outcomes
/ Practical Application
/ Preferences
/ Public Health
/ Quality of Life Research
2019
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Including Opt-Out Options in Discrete Choice Experiments: Issues to Consider
by
Campbell, Danny
, Erdem, Seda
in
Algorithms
/ Behavior
/ Choice Behavior
/ Experiments
/ Health Administration
/ Health Economics
/ Health Services Research
/ Humans
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Monte Carlo Method
/ Monte Carlo simulation
/ Patient Preference
/ Pharmacoeconomics and Health Outcomes
/ Practical Application
/ Preferences
/ Public Health
/ Quality of Life Research
2019
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Including Opt-Out Options in Discrete Choice Experiments: Issues to Consider
by
Campbell, Danny
, Erdem, Seda
in
Algorithms
/ Behavior
/ Choice Behavior
/ Experiments
/ Health Administration
/ Health Economics
/ Health Services Research
/ Humans
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Monte Carlo Method
/ Monte Carlo simulation
/ Patient Preference
/ Pharmacoeconomics and Health Outcomes
/ Practical Application
/ Preferences
/ Public Health
/ Quality of Life Research
2019
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Including Opt-Out Options in Discrete Choice Experiments: Issues to Consider
Journal Article
Including Opt-Out Options in Discrete Choice Experiments: Issues to Consider
2019
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Overview
Providing an opt-out alternative in discrete choice experiments can often be considered to be important for presenting real-life choice situations in different contexts, including health. However, insufficient attention has been given to how best to address choice behaviours relating to this opt-out alternative when modelling discrete choice experiments, particularly in health studies. The objective of this paper is to demonstrate how to account for different opt-out effects in choice models. We aim to contribute to a better understanding of how to model opt-out choices and show the consequences of addressing the effects in an incorrect fashion. We present our code written in the R statistical language so that others can explore these issues in their own data. In this practical guideline, we generate synthetic data on medication choice and use Monte Carlo simulation. We consider three different definitions for the opt-out alternative and four candidate models for each definition. We apply a frequentist-based multimodel inference approach and use performance indicators to assess the relative suitability of each candidate model in a range of settings. We show that misspecifying the opt-out effect has repercussions for marginal willingness to pay estimation and the forecasting of market shares. Our findings also suggest a number of key recommendations for DCE practitioners interested in exploring these issues. There is no unique best way to analyse data collected from discrete choice experiments. Researchers should consider several models so that the relative support for different hypotheses of opt-out effects can be explored.
Publisher
Springer International Publishing,Springer Nature B.V
Subject
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