Objectives: The aim of this study was to assess the performance and impact of multilevel modelling (MLM) compared with ordinary least squares (OLS) regression in trial-based economic evaluations with clustered data. Methods: Three thousand datasets with balanced and unbalanced clusters were simulated with correlation coefficients between costs and effects of − 0.5, 0, and 0.5, and intraclass correlation coefficients (ICCs) varying between 0.05 and 0.30. Each scenario was analyzed using both MLM and OLS. Statistical uncertainty around MLM and OLS estimates was estimated using bootstrapping. Performance measures were estimated and compared between approaches, including bias, root mean squared error (RMSE) and coverage probability. Cost and effect differences, and their corresponding confidence intervals and standard errors, incremental cost-effectiveness ratios, incremental net-monetary benefits and cost-effectiveness acceptability curves were compared. Results: Cost-effectiveness outcomes were similar between OLS and MLM. MLM produced larger statistical uncertainty and coverage probabilities closer to nominal levels than OLS. The higher the ICC, the larger the effect on statistical uncertainty between MLM and OLS. Significant cost-effectiveness outcomes as estimated by OLS became non-significant when estimated by MLM. At all ICCs, MLM resulted in lower probabilities of cost effectiveness than OLS, and this difference became larger with increasing ICCs. Performance measures and cost-effectiveness outcomes were similar across scenarios with varying correlation coefficients between costs and effects. Conclusions: Although OLS produced similar cost-effectiveness outcomes, it substantially underestimated the amount of variation in the data compared with MLM. To prevent suboptimal conclusions and a possible waste of scarce resources, it is important to use MLM in trial-based economic evaluations when data are clustered.