OBJECTIVES: In previous work, we explored through computer-aided simulated clinical trials how heterogeneity of baseline risk can lead to heterogeneity of treatment-effect under a variety of assumptions. We now explore how heterogeneity of competing risks affect treatment-effect heterogeneity under a variety of assumptions. METHODS: Using simulated clinical trials in which the intervention has a constant effect on disease-specific risk (odds ratio = 0.7) but no effect on competing risk and in which outcomes in individuals are determined by varying 2 parameters: (1) the overall risk of the outcome of interest and (2) the ratio between the competing risk and the disease-specific (i.e. treatment-responsive) risk. RESULTS: Under conditions in the simulations, the odds ratio of the treatment-effect on the overall outcome is highly dependent on the ratio of the competing and disease-specific risk, decreasing as this ratio increases. Although the absolute treatment-effect increases with increasing overall risk, the odds ratio for the treatment decreases as the overall risk increases (holding constant the ratio between disease-specific and competing risk). When disease-specific outcomes are measured, a similar relationship between treatment-effect and overall risk is observed, although the decrease in the odds ratio with increasing risk is greatly attenuated. Detecting significant treatment-effect heterogeneity (on the odds ratio scale) based on competing risk is likely to occur only when competing risk is very high or when patients can be sub-grouped by variables which distinguish between disease-specific and competing risk. CONCLUSION: The ratio of competing risk to disease-specific risk in a population can have an important impact on the measured treatment effect, even when disease-specific outcomes are measured. Detection of competing-risk-based treatment-effect heterogeneity may depend on the identification of risk factors that differentiate disease-specific from competing risk. Simulations can be useful to anticipate the magnitude of these effects when planning a clinical trial.