To what extent does the use of crosswalks instead of EQ-5D value sets impact reimbursement decisions?: a simulation study.

PURPOSE: Inconsistent results have been found on the impact of using crosswalks versus EQ-5D value sets on reimbursement decisions. We sought to further investigate this issue in a simulation study. METHODS: Trial-based economic evaluation data were simulated for different conditions (depression, low back pain, osteoarthritis, cancer), severity levels (mild, moderate, severe), and effect sizes (small, medium, large). For all 36 scenarios, utilities were calculated using 3L and 5L value sets and crosswalks (3L to 5L and 5L to 3L crosswalks) for the Netherlands, the United States, and Japan. Utilities, quality-adjusted life years (QALYs), incremental QALYs, incremental cost-effectiveness ratios (ICERs), and probabilities of cost-effectiveness (pCE) obtained from values sets and crosswalks were compared. RESULTS: Differences between value sets and crosswalks ranged from -0.33 to 0.13 for utilities, from -0.18 to 0.13 for QALYs, and from -0.01 to 0.08 for incremental QALYs, resulting in different ICERs. For small effect sizes, at a willingness-to-pay of €20,000/QALY, the largest pCE difference was found for moderate cancer between the Japanese 5L value set and 5L to 3L crosswalk (difference = 0.63). For medium effect sizes, the largest difference was found for mild cancer between the Japanese 3L value set and 3L to 5L crosswalk (difference = 0.06). For large effect sizes, the largest difference was found for mild osteoarthritis between the Japanese 3L value set and 3L to 5L crosswalk (difference = 0.08). CONCLUSION: The use of crosswalks instead of EQ-5D value sets can impact cost-utility outcomes to such an extent that this may influence reimbursement decisions.

The handling of missing data in trial-based economic evaluations: should data be multiply imputed prior to longitudinal linear mixed-model analyses?

INTRODUCTION: For the analysis of clinical effects, multiple imputation (MI) of missing data were shown to be unnecessary when using longitudinal linear mixed-models (LLM). It remains unclear whether this also applies to trial-based economic evaluations. Therefore, this study aimed to assess whether MI is required prior to LLM when analyzing longitudinal cost and effect data. METHODS: Two-thousand complete datasets were simulated containing five time points. Incomplete datasets were generated with 10, 25, and 50% missing data in follow-up costs and effects, assuming a Missing At Random (MAR) mechanism. Six different strategies were compared using empirical bias (EB), root-mean-squared error (RMSE), and coverage rate (CR). These strategies were: LLM alone (LLM) and MI with LLM (MI-LLM), and, as reference strategies, mean imputation with LLM (M-LLM), seemingly unrelated regression alone (SUR-CCA), MI with SUR (MI-SUR), and mean imputation with SUR (M-SUR). RESULTS: For costs and effects, LLM, MI-LLM, and MI-SUR performed better than M-LLM, SUR-CCA, and M-SUR, with smaller EBs and RMSEs as well as CRs closers to nominal levels. However, even though LLM, MI-LLM and MI-SUR performed equally well for effects, MI-LLM and MI-SUR were found to perform better than LLM for costs at 10 and 25% missing data. At 50% missing data, all strategies resulted in relatively high EBs and RMSEs for costs. CONCLUSION: LLM should be combined with MI when analyzing trial-based economic evaluation data. MI-SUR is more efficient and can also be used, but then an average intervention effect over time cannot be estimated.