Pin, Lukas, Sverdlov, Alex, Bretz, Frank and Bornkamp, Bjoern (2025) Randomization-Based Inference for MCP-Mod. Statistics in medicine, 44 (10-12). e70092. ISSN 1097-0258

Abstract

Dose selection is critical in pharmaceutical drug development, as it directly impacts therapeutic efficacy and patient's safety of a drug. The Generalized Multiple Comparison Procedures and Modeling approach is commonly used in Phase II trials for testing and estimation of dose-response relationships. However, its effectiveness in small sample sizes, particularly with binary endpoints, is hindered by issues like complete separation in logistic regression, leading to non existence of estimates. Motivated by an actual clinical trial using the MCP-Mod approach, this paper introduces penalized maximum likelihood estimation (MLE) and randomization-based inference techniques to address these challenges. Randomization-based inference allows for exact finite sample inference, while population-based inference for MCP-Mod typically relies on asymptotic approximations. Simulation studies demonstrate that randomization-based tests can enhance statistical power in small to medium-sized samples while maintaining control over type-I error rates, even in the presence of time trends. Our results show that residual-based randomization tests using penalized MLEs not only improve computational efficiency but also outperform standard randomization-based methods, making them an adequate choice for dose-finding analyses within the MCP-Mod framework. Additionally, we apply these methods to pharmacometric settings, demonstrating their effectiveness in such scenarios. The results in this paper underscore the potential of randomization-based inference for the analysis of dose-finding trials, particularly in small sample contexts.

Item Type:	Article
Keywords:	Humans Likelihood Functions Computer Simulation Dose-Response Relationship, Drug Randomized Controlled Trials as Topic Sample Size Models, Statistical Clinical Trials, Phase II as Topic Random Allocation
Date Deposited:	08 Oct 2025 00:45
Last Modified:	08 Oct 2025 00:45
URI:	https://oak.novartis.com/id/eprint/55503