Abstract
AbstractMCP-Mod (Multiple Comparison Procedure-Modelling) is an efficient statistical method for the analysis of Phase II dose-finding trials, although it requires specialised expertise to pre-specify plausible candidate models along with model parameters. This can be problematic given limited knowledge of the agent/compound being studied. Misspecification of candidate models and model parameters can severely degrade its performance. To circumvent this challenge, in this work, we introduce MAP-curvature, a Bayesian model-free approach for the detection of the dose-response signal in Phase II dose-finding trials. MAP-curvature is built upon a Bayesian hierarchical method incorporating information about the total curvature of the dose-response curve. Through extensive simulations, we show that MAP-curvature has comparable performance to MCP-Mod if the true underlying dose-response model is included in the candidate model set of MCP-Mod. Otherwise, MAP-curvature can achieve performance superior to that of MCP-Mod, especially when the true dose-response model drastically deviates from candidate models in MCP-Mod.
Publisher
Cold Spring Harbor Laboratory
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