Web-based surveys are increasingly utilized for health valuation studies but may be more prone to lack of engagement and, therefore, poor data validity. The objective of this study was to evaluate the effect of imposed engagement (i.e., at least three trade-offs) in the composite time trade-off (cTTO) task.
The EQ-5D-5L valuation study protocol and study design were adapted for online, unsupervised completion in two arms: base case and engagement. Validity of preferences was assessed using the prevalence of inconsistent valuations and expected patterns of TTO values. Respondent task engagement was measured using time per task. Value sets were generated using linear regression with a random intercept (RILR).
The base case (n = 501) and engagement arms (n = 504) clustered at different TTO values: [base case] 0, 1; [engagement] -0.5, 0.45, 0.6. Mean TTO values were lower for the engagement arm. Engagement respondents did not spend more time per TTO task: [base case] 63.3 s (SD 77.9 s); [engagement] 64.7 s (SD 73.3 s); p = 0.36. No significant difference was found between arms for prevalence of respondents with at least one inconsistent TTO value: [base case] 61.1%; [engagement] 63.5%; p = 0.43. Both value sets had significant intercepts far from 1: [base case] 0.846; [engagement] 0.783. The relative importance of the EQ-5D dimensions also differed between arms.
Both online arms had poor quality data. A minimum trade-off threshold did not improve engagement nor face validity of the data, indicating that modifications to the number of iterations are insufficient alone to improve data quality/validity of online TTO studies.
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Funding for data collection was supported by Bristol-Myers Squibb. R Jiang was supported by fellowships from the PhRMA Foundation and a Dean’s Scholar Award from the University of Illinois at Chicago during her PhD, during the time of study conduct and analysis.
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Jiang, R., Kohlmann, T., Lee, T.A. et al. Increasing respondent engagement in composite time trade-off tasks by imposing three minimum trade-offs to improve data quality. Eur J Health Econ 22, 17–33 (2021). https://doi.org/10.1007/s10198-020-01224-6