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Related Experiment Video

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Choice and No-Choice Bioassays to Study the Pupation Preference and Emergence Success of Ectropis grisescens

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Accounting for Scale Heterogeneity in Healthcare-Related Discrete Choice Experiments when Comparing Stated

Stuart J Wright¹, Caroline M Vass¹, Gene Sim¹

¹Manchester Centre for Health Economics, Division of Population Health, Health Services Research and Primary Care, The University of Manchester, Jean McFarlane Building, Oxford Road, Manchester, M13 9PL, UK.

|March 2, 2018

Summary

Scale heterogeneity is common in healthcare discrete choice experiments (DCEs) comparing groups. Few studies formally address this, risking biased preference results.

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Area of Science:

Health economics
Decision science
Preference measurement

Background:

Scale heterogeneity, or differing error variances in choices, significantly impacts discrete choice experiment (DCE) results when comparing respondent groups.
Understanding and addressing scale heterogeneity is crucial for accurate preference analysis.

Purpose of the Study:

To systematically review how scale heterogeneity has been addressed in healthcare DCEs comparing different groups.
To identify the prevalence of scale heterogeneity and the analytical methods used to account for it.

Main Methods:

Systematic review of healthcare DCEs published between 1990 and February 2016.
Screening of full-text articles to identify studies comparing preferences across multiple groups.
Data extraction on scale heterogeneity testing and analytical methods, followed by narrative analysis.

Main Results:

Of 626 identified DCEs, 278 compared groups. Only 18% discussed scale issues, 7% used formal analysis for scale differences, and 2% accounted for it in analysis.
Scale heterogeneity was present in 65% of studies that tested for it.
Various analytical methods were employed, including heteroscedastic conditional logit models and scale-adjusted latent class analysis.

Conclusions:

Scale heterogeneity is a prevalent, yet under-addressed, issue in healthcare DCEs.
Failure to formally test and account for scale heterogeneity risks biased and misleading conclusions on healthcare preferences.
Formal methods for scale heterogeneity analysis should be standard practice in comparative DCEs.