AbstractCharacterizing an appropriate dose‐response relationship and identifying the right dose in a clinical trial are two main goals of early drug‐development. MCP‐Mod is one of the pioneer approaches developed within the last 10 years that combines the modeling techniques with multiple comparison procedures to address the above goals in clinical drug development. The MCP‐Mod approach begins with a set of potential dose‐response models, tests for a significant dose‐response effect (proof of concept, PoC) using multiple linear contrasts tests and selects the “best” model among those with a significant contrast test. A disadvantage of the method is that the parameter values of the candidate models need to be fixed a priori for the contrasts tests. This may lead to a loss in power and unreliable model selection. For this reason, several variations of the MCP‐Mod approach and a hierarchical model selection approach have been suggested where the parameter values need not be fixed in the proof of concept testing step and can be estimated after the model selection step. This paper provides a numerical comparison of the different MCP‐Mod variants and the hierarchical model selection approach with regard to their ability of detecting the dose‐response trend, their potential to select the correct model and their accuracy in estimating the dose response shape and minimum effective dose. Additionally, as one of the approaches is based on two‐sided model comparisons only, we make it more consistent with the common goals of a PoC study, by extending it to one‐sided comparisons between the constant and alternative candidate models in the proof of concept step.