Published Jun 2016
Citation
Hauber AB, González JM, Groothuis-Oudshoorn CM, et al. Statistical methods for the analysis of discrete choice experiments: a report of the ISPOR Conjoint Analysis Good Research Practices Task Force. Value Health 2016;19(4):300-315.
Abstract
Conjoint analysis is a stated-preference survey method that can be used
to elicit responses that reveal preferences, priorities, and the relative
importance of individual features associated with health care interventions
or services. Conjoint analysis methods, particularly discrete choice
experiments (DCEs), have been increasingly used to quantify preferences
of patients, caregivers, physicians, and other stakeholders. Recent
consensus-based guidance on good research practices, including two
recent task force reports from the International Society for Pharmacoeconomics
and Outcomes Research, has aided in improving the quality of
conjoint analyses and DCEs in outcomes research. Nevertheless, uncertainty
regarding good research practices for the statistical analysis of
data from DCEs persists.
There are multiple methods for analyzing DCE
data. Understanding the characteristics and appropriate use of different
analysis methods is critical to conducting a well-designed DCE study.
This report will assist researchers in evaluating and selecting among
alternative approaches to conducting statistical analysis of DCE data.
We first present a simplistic DCE example and a simple method for
using the resulting data. We then present a pedagogical example of a
DCE and one of the most common approaches to analyzing data from
such a question format—conditional logit. We then describe some
common alternative methods for analyzing these data and the
strengths and weaknesses of each alternative. We present the
ESTIMATE checklist, which includes a list of questions to consider
when justifying the choice of analysis method, describing the analysis,
and interpreting the results.
Keywords: conjoint analysis, discrete choice experiment, stated preference
methods, statistical analysis.
Copyright © 2017, International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc.
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