For the analysis of clinical effects, multiple imputation (MI) of missing data was shown to be unnecessary when using longitudinal linear mixed-models (LLM). It remains unclear whether this also applies to cost estimates from trial-based economic evaluations, that are generally right-skewed. Therefore, this study aimed to assess whether MI is required prior to LLM when analyzing longitudinal cost-effectiveness data.

Two-thousand complete datasets were simulated containing five time points. Incomplete datasets were generated with 10 percent, 25 percent, and 50 percent missing data in costs and effects, assuming a Missing At Random (MAR) mechanism. Statistical performance of six different methodological strategies was compared in terms of empirical bias (EB), root-mean-squared error (RMSE), and coverage rate (CR). Six strategies were compared: (i) LLM (LLM), (ii) MI prior to LLM (MI-LLM), (iii) mean imputation prior to LLM (M-LLM), (iv) complete-case analysis prior to seemingly unrelated regression (CCA-SUR), (v) MI prior to SUR (MI-SUR), and (vi) mean imputation prior to SUR (M-SUR). To evaluate the impact on the probability of cost-effectiveness at different willingness-to-pay [WTPs] thresholds, cost-effectiveness analyses were performed by applying the six strategies to two empirical datasets with 9% and 50% of missing data, respectively.

For costs and effects, LLM, MI-LLM, and MI-SUR performed better than M-LLM, CCA-SUR, and M-SUR, as indicated by smaller EBs and RMSEs, as well as CRs closer to the nominal levels of 0.95. 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 percent and 25 percent missing data. At 50 percent missing data, all strategies resulted in relatively high EBs and RMSEs for costs. In both empirical datasets, LLM, MI-LLM, and MI-SUR all resulted in similar probabilities of cost-effectiveness at different WTPs.

When opting for using LLM for analyzing trial-based economic evaluation data, researchers are advised to multiply impute missing values first. Otherwise, MI-SUR may also be used.