Influence of the Size of Cohorts in Adaptive Design for Nonlinear Mixed Effects Models: An Evaluation by Simulation for a Pharmacokinetic and Pharmacodynamic Model for a Biomarker in Oncology
- 255 Downloads
In this study we aimed to evaluate adaptive designs (ADs) by clinical trial simulation for a pharmacokinetic-pharmacodynamic model in oncology and to compare them with one-stage designs, i.e., when no adaptation is performed, using wrong prior parameters.
We evaluated two one-stage designs, ξ0 and ξ*, optimised for prior and true population parameters, Ψ0 and Ψ*, and several ADs (two-, three- and five-stage). All designs had 50 patients. For ADs, the first cohort design was ξ0. The next cohort design was optimised using prior information updated from the previous cohort. Optimal design was based on the determinant of the Fisher information matrix using PFIM. Design evaluation was performed by clinical trial simulations using data simulated from Ψ*.
Estimation results of two-stage ADs and ξ * were close and much better than those obtained with ξ 0. The balanced two-stage AD performed better than two-stage ADs with different cohort sizes. Three- and five-stage ADs were better than two-stage with small first cohort, but not better than the balanced two-stage design.
Two-stage ADs are useful when prior parameters are unreliable. In case of small first cohort, more adaptations are needed but these designs are complex to implement.
KEY WORDSadaptive design Fisher information matrix nonlinear mixed effects model optimal design pharmacokinetic-pharmacodynamic
Fisher information matrix
Nonlinear mixed effects model
Relative estimation error
Relative root mean squared error
Transforming growth factor β
ACKNOWLEDGMENTS AND DISCLOSURES
The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n° 115156, resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. The DDMoRe project is also financially supported by contributions from Academic and SME partners. This work does not necessarily represent the view of all DDMoRe partners.
- 1.Lavielle M. Mixed effects models for the population approach: models, tasks, methods and tools. Chapman and Hall/CRC; 2014. 383 p.Google Scholar
- 7.Mentré F, Chenel M, Comets E, Grevel J, Hooker A, Karlsson M, et al. Current use and developments needed for optimal design in pharmacometrics: a study performed among DDMoRe’s european federation of pharmaceutical industries and associations members. CPT Pharmacometrics Syst Pharmacol. 2013;2(6):e46.PubMedCentralCrossRefPubMedGoogle Scholar
- 8.Nyberg J, Bazzoli C, Ogungbenro K, Aliev A, Leonov S, Duffull S, et al. Methods and software tools for design evaluation for population pharmacokinetics-pharmacodynamics studies. Br J Clin Pharmacol. 2014.Google Scholar
- 10.Mentré F, Thu Thuy N, Lestini G, Dumont C, PFIM group. PFIM 4.0: new features for optimal design in nonlinear mixed effects models using R. PAGE 2014 Abstr 3032 [Internet]. Available from: [http://www.page-meeting.org/default.asp?abstract=3032]
- 18.Chang M. Adaptive design theory and implementation using SAS and R. 1st ed. Boca Raton: Chapman and Hall/CRC; 2007. 440.Google Scholar
- 23.Dumont C, Chenel M, Mentré F. Two-stage adaptive design in nonlinear mixed effects models: application to pharmacokinetics in children. Commun Stat. ACCEPTED.Google Scholar
- 24.Bueno L, de Alwis D, Pitou C, Yingling J, Lahn M, Glatt S, et al. Semi-mechanistic modelling of the tumour growth inhibitory effects of LY2157299, a new type I receptor TGF-beta kinase antagonist, in mice. Eur J Cancer Oxf Engl 1990. 2008;44(1):142–50.Google Scholar
- 26.Mielke T, Schwabe R. Some considerations on the fisher information in nonlinear mixed effects models. In: Giovagnoli A, Atkinson AC, Torsney B, May C, editors. mODa 9 – Advances in Model-oriented design and analysis [Internet]. Physica-Verlag HD; 2010 [cited 2014 Sep 2]. p. 129–36. Available from: http://link.springer.com.gate2.inist.fr/chapter/ 10.1007/978-3-7908-2410-0_17.