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

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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.

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Acknowledgements

The authors are grateful to two anonymous referees for their constructive comments on an earlier version of the paper which helped to improve the presentation of the results.

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Authors and Affiliations

  1. Biostatistics, Quantitative Clinical Sciences and Reporting, CSL Behring, 1020 1st Ave, King of Prussia, PA, 19406, USA

    Younan Chen, Michael Fries & Sergei Leonov

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  1. Younan Chen
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  2. Michael Fries
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  3. Sergei Leonov
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Correspondence to Sergei Leonov.

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Chen, Y., Fries, M. & Leonov, S. Longitudinal model for a dose-finding study for a rare disease treatment. Stat Papers 64, 1343–1360 (2023). https://doi.org/10.1007/s00362-023-01424-1

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  • DOI: https://doi.org/10.1007/s00362-023-01424-1

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