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The New England Journal of Statistics in Data Science


LiMAP-Curvature: A Simple Model-Free Approach for Analysing Dose-Finding Studies
Volume 3, Issue 3 (2025), pp. 265–271
Pub. online: 10 September 2025      Type: Methodology Article      Open accessOpen Access
Area: Statistical Methodology

1 Present address: The George Institute for Global Health, Imperial College London, London W12 7RZ, UK
Accepted
12 June 2025
Published
10 September 2025

Abstract

MCP-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, and misspecification of candidate models and model parameters can severely degrade its performance. To circumvent this challenge, in the work, we introduce LiMAP-curvature, a Bayesian model-free approach for the detection of the dose-response signal in Phase II dose-finding trials. LiMAP-curvature is built upon a Bayesian hierarchical framework incorporating information about the total curvature of the dose-response curve. Through extensive simulations, we show that LiMAP-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, LiMAP-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.

Supplementary material

 Supplementary Material
Supplementary Tables and Figures.

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Copyright
© 2025 New England Statistical Society
Open access article under the CC BY license.

Keywords
Dose-finding trial Dose-response signal detection Model-free approach Bayesian hierarchical model Curvature prior

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