Longitudinal model for a dose-finding study for a rare disease treatment

Younan Chen (), Michael Fries () and Sergei Leonov ()
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Younan Chen: CSL Behring
Michael Fries: CSL Behring
Sergei Leonov: CSL Behring

Statistical Papers, 2023, vol. 64, issue 4, No 17, 1343-1360

Abstract: Abstract Dose-finding studies in rare diseases are faced with unique challenges including low patient numbers, limited understanding of the dose-exposure-response relationship, variability around the endpoints. In addition, patient exposure to placebo is often not feasible. To describe the disease progression for different dose groups, we introduce a longitudinal model for the change from baseline for a clinical endpoint. We build a nonlinear mixed effects model using the techniques which have become popular over the past two decades in the design and analysis of population pharmacokinetic/pharmacodynamics studies. To evaluate operating characteristics of the proposed design, we derive the Fisher information matrix and validate analytical results via simulations. Alternative considerations, such as trend analysis, are discussed as well.

Keywords: Dose-finding; Nonlinear mixed effects model; Longitudinal model; Optimal design; D-optimality; $$ED_p$$ E D p -optimality; 62K05; 62P10 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00362-023-01424-1

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