Bayesian model averaging of longitudinal dose-response models

ABSTRACT

Selecting a safe and clinically beneficial dose can be difficult in drug development. Dose justification often relies on dose-response modeling where parametric assumptions are made in advance which may not adequately fit the data. This is especially problematic in longitudinal dose-response models, where additional parametric assumptions must be made. This paper proposes a class of longitudinal dose-response models to be used in the Bayesian model averaging paradigm which improve trial operating characteristics while maintaining flexibility a priori. A new longitudinal model for non-monotonic longitudinal profiles is proposed. The benefits and trade-offs of the proposed approach are demonstrated through a case study and simulation.

KEYWORDS:

• dose response
• Bayesian model averaging
• longitudinal modeling
• dose selection
• clinical trials

Acknowledgements

The authors would like to thank two anonymous referees and the associate editor for their comments and suggestions which improved the presentation of the content in this paper.

Disclosure statement

All authors completed this research while employed by Eli Lilly and Company. Richard Payne and Pallavi Ray are shareholders of Eli Lilly and Company.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.