OBJECTIVES

: To compare the performance of 3 different elicitation methods in an online survey environment in China.

METHODS

: An online panel of general population in China participated in a web survey. Each participant was randomized to complete two out of three preference elicitation technique tasks, which were 8 random Discrete Choice Experiment (DCE) tasks, 6 random Time Trade Off (TTO) tasks, and 10 random Best Worst Scaling (BWS) tasks. Ordinal method results were self-anchored on the death values to estimate the utilities. Models assuming homogeneous preference (Conditional Logit and linear regressions (OLS)) and preference heterogeneity (Hierarchical Bayes and Mixed Effects Linear Hierarchical Bayes) were used. The number of significant levels, percent certainty and Root Likelihood (RLH) were employed for the comparison

RESULTS

: A total of 961 participants (40.6% <40 years, 49 % female, 76.4% healthy) who passed the quality checks were utilized in the analysis. Based on the number of significant levels, percent certainty and RLH, the BWS homogenous preference model was the best (0, 30.5%, 0.39) compared to DCE (6, 20%, 0.38) and TTO (17, 26%, 0.27). The worst state (PITS) was valued at 0.17, -0.69 and -0.033 respectively. The TTO heterogenous preference model had a whole dimension insignificant. Between BWS and DCE, the preferred model specifications changed for the DCE and both had none insignificant levels. The Percent certainty was 70 & 55.2, RLH at 0.58 & 0.61 and PITS valued at 0.197 and 0.359 for BWS and DCE models respectively

CONCLUSIONS

: Using online DCE or BWS to elicit preference in china is feasible while TTO requires further optimization. Data from ordinal methods modeled by heterogenous preference were more consistent than the one from the cardinal method (TTO) or modeled assuming homogenous preference. Cardinal methods still perform worse than ordinal methods in online surveys and generates less reliable results.