Comparison of Model Averaging and Model Selection in Dose Finding Trials Analyzed by Nonlinear Mixed Effect Models
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In drug development, pharmacometric approaches consist in identifying via a model selection (MS) process the model structure that best describes the data. However, making predictions using a selected model ignores model structure uncertainty, which could impair predictive performance. To overcome this drawback, model averaging (MA) takes into account the uncertainty across a set of candidate models by weighting them as a function of an information criterion. Our primary objective was to use clinical trial simulations (CTSs) to compare model selection (MS) with model averaging (MA) in dose finding clinical trials, based on the AIC information criterion. A secondary aim of this analysis was to challenge the use of AIC by comparing MA and MS using five different information criteria. CTSs were based on a nonlinear mixed effect model characterizing the time course of visual acuity in wet age-related macular degeneration patients. Predictive performances of the modeling approaches were evaluated using three performance criteria focused on the main objectives of a phase II clinical trial. In this framework, MA adequately described the data and showed better predictive performance than MS, increasing the likelihood of accurately characterizing the dose-response relationship and defining the minimum effective dose. Moreover, regardless of the modeling approach, AIC was associated with the best predictive performances.
KEY WORDSdose-response relationship model averaging model selection nonlinear mixed effect models
This work was financed by a CIFRE agreement (Conventions Industrielles de Formation par la Recherche) and was conducted under the supervision of the ANRT (Association Nationale Recherche Technologie). The CIFRE agreement is a partnership between a public laboratory and a company, here the UMR 1137 and INSTITUT ROCHE, respectively.
- 3.Musuamba FT, Manolis E, Holford N, Cheung S, Friberg LE, Ogungbenro K, et al. Advanced methods for dose and regimen finding during drug development: summary of the EMA/EFPIA workshop on dose finding (London 4–5 December 2014). CPT Pharmacomet Syst Pharmacol. 2017;6(7):418–29.Google Scholar
- 4.Dose-response information to support drug registration (ICH Harmonized Tripartite Guideline), Page 2. 1994 [cited 2017 Jun 14]. Available from: http://www.ich.org/products/guidelines/efficacy/efficacy-single/article/dose-response-information-to-support-drug-registration.html
- 5.Bornkamp B, Bretz F, Dmitrienko A, Enas G, Gaydos B, Hsu C-H, et al. Innovative approaches for designing and analyzing adaptive dose-ranging trials. J Biopharm Stat. 2007;17(6):965–95.Google Scholar
- 16.Bates DM. Nonlinear mixed effects models for longitudinal data. In: Wiley StatsRef: Statistics Reference Online [Internet]. John Wiley & Sons, Ltd; 2014. Available from: doi: https://doi.org/10.1002/9781118445112.stat05806/abstract
- 19.Aoki Y, Röshammar D, Hamrén B, Hooker AC. Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection. J Pharmacokinet Pharmacodyn 2017; 1–17.Google Scholar
- 21.Claeskens G, Hjort NL. Model selection and model averaging. 1 edition. Cambridge; New York: Cambridge University Press; 2008. 332 p.Google Scholar
- 22.Rosenfeld PJ, Brown DM, Heier JS, Boyer DS, Kaiser PK, Chung CY, et al. Ranibizumab for neovascular age-related macular degeneration. N Engl J Med. 2006;355(14):1419–31.Google Scholar
- 24.Kullback S. Information theory and statistics. New edition ed. Mineola, N.Y: Dover Publications; 1997. p. 432.Google Scholar
- 25.MacKay DJC. Information theory, inference and learning algorithms. 1st ed. Cambridge, UK; New York: Cambridge University Press; 2003. p. 640.Google Scholar
- 26.Beal S, Sheiner LB, Boeckmann A, Bauer RJ. NONMEM user’s guides. (1989–2009). Ellicott City, MD USA: Icon Development Solutions; 2009.Google Scholar