CLRT‐Mod: An efficient methodology for pharmacometric model‐based analysis of longitudinal phase II dose finding studies under model uncertainty. (2nd March 2021)

• Record Type: Journal Article
• Authors: Buatois, Simon; Ueckert, Sebastian; Frey, Nicolas; Retout, Sylvie; Mentré, France
• Is Part Of: Statistics in medicine. Volume 40:Number 10(2021)
• Page Start: 2435
• Page End: 2451
• Publication Date: 2021-03-02
• Subjects: dose‐response -- effects model -- LRT -- MCP‐Mod -- model averaging -- nonlinear mixed; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8913
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Within the challenging context of phase II dose‐finding trials, longitudinal analyses may increase drug effect detection power compared to an end‐of‐treatment analysis. This work proposes cLRT‐Mod, a pharmacometric adaptation of the MCP‐Mod methodology, which allows the use of nonlinear mixed effect models to first detect a dose‐response signal and then identify the doses for the confirmatory phase while accounting for model structure uncertainty. The method was evaluated through extensive clinical trial simulations of a hypothetical phase II dose‐finding trial using different scenarios and comparing different methods such as MCP‐Mod. The results show an increase in power using cLRT with longitudinal data compared to an EOT multiple contrast tests for scenarios with small sample size and weak drug effect while maintaining pre‐specifiability of the models prior to data analysis and the nominal type I error. This work shows how model averaging provides better coverage probability of the drug effect in the prediction step, and avoids under‐estimation of the size of the confidence interval. Finally, for illustration purpose cLRT‐Mod was applied to the analysis of a real phase II dose‐finding trial.