OBJECTIVES: Medical interventions are often administered as treatment combinations (e.g., drug A + drug B). Models for effects of treatment combinations in multiple treatment comparison meta-analysis (MTCs) have previously been proposed. The objective of this study was to evaluate the comparative statistical performance of the conventional MTC model and an ‘additive effects’ MTC model, in scenarios where ‘additivity’ holds true (i.e., where the effect of effect of treatment A and B combined is equal to the sum of the individual effects of treatment A and treatment B) and in scenarios where ‘additivity’ is mildly or strongly violated. METHODS: We simulated MTC scenarios where additivity held true or was violated. We applied conventional and additive effects Bayesian MTC models to the simulated data. We measured the proportion of over- and underestimated treatment effects, the coverage of the 95% credible intervals, and the statistical power.  RESULTS: Under true additivity, the additive effects model is superior to the conventional model. Under mildly violated additivity, the additive model is less accurate (more over- and underestimates), but more precise (comparable coverage and greater power). Under strongly violated additivity, the additive model performs worse in terms of accuracy and coverage. CONCLUSIONS: The additive model may readily be used in practice when approximate additivity can be assumed.