OBJECTIVES: In randomized controlled trials, differences in prognostic factors – whether statistically significant or not – contribute to absolute differences in outcomes. Absolute differences are at the heart of economic evaluation. Economic modelling may help increase precision of incremental differences, despite randomization. The objective of the present study was to describe the effect of economic modelling techniques on the magnitude and precision of economic and decision outcomes using a RCT-based economic evaluation. METHODS: An economic evaluation was conducted alongside a RCT (n=350) in diagnostic interventional cardiology. Raw unadjusted total costs and QALYs were assembled at the individual level using resource use and EQ5D responses. For economic modelling, outcomes were then conditioned according to the diagnosis and fit with generalized linear models, adjusting for baseline characteristics. Total costs and QALYs were then estimated using marginal prediction with the fitted models. Family and link functions were selected using the Modified Park’s and Pregibon Link test, respectively. Uncertainty in GLM coefficients, unit cost parameters and sampling were incorporated using bootstrapping and Monte Carlo methods. RESULTS: The magnitude and direction of incremental costs were comparable between the raw vs. modelled results (-£132 vs. -£204). However, precision increased considerably; the 95%CI reduced by 44% ([-£1772 to £817] vs. [-£1437 to £30]). Incremental QALYs also showed comparable magnitudes (0.013 vs -0.005), though the direction reversed, albeit by a non-important magnitude. As well, the 95%CI of incremental QALYs reduced by 83% ([-0.033 to 0.060] vs. [-0.015 to 0.001]). Reduction in joint incremental cost-effect uncertainty was also apparent upon visual inspection of the cost-effectiveness plane. Decision (cost-effectiveness) uncertainty was comparable across the common willingness-to-pay thresholds (~70% at £0-£30,000/QALY). CONCLUSIONS: Economic modelling can increase precision in economic outcomes and reduce uncertainty in decision making, supporting the results and decision arising from a raw unadjusted economic evaluation alongside a RCT.