134
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Bayesian model averaging of longitudinal dose-response models

, &
Pages 349-365 | Received 29 Oct 2021, Accepted 02 Dec 2023, Published online: 17 Dec 2023

References

  • Akaike, H. 1998. Information theory and an extension of the maximum likelihood principle. In Parzen, E., Tanabe, K., Kitagawa, G. (Eds.), Selected papers of Hirotugu Akaike, pp. 199–213. New York, NY: Springer. https://doi.org/10.1007/978-1-4612-1694-0_15
  • Ando, T., and R. Tsay. 2010. Predictive likelihood for Bayesian model selection and averaging. International Journal of Forecasting 26 (4):744–763. doi:10.1016/j.ijforecast.2009.08.001.
  • Bretz, F., J. C. Pinheiro, and M. Branson. 2005. Combining multiple comparisons and modeling techniques in dose-response studies. Biometrics 61 (3):738–748. doi:10.1111/j.1541-0420.2005.00344.x.
  • Chib, S. 1995. Marginal likelihood from the Gibbs output. Journal of the American Statistical Association 90 (432):1313–1321. doi:10.1080/01621459.1995.10476635.
  • Dette, H., S. Titoff, S. Volgushev, and F. Bretz. 2015. Dose response signal detection under model uncertainty. Biometrics 71 (4):996–1008. doi:10.1111/biom.12357.
  • European Medicines Agency. 2015 Report from dose finding workshop. https://www.ema.europa.eu/en/documents/report/report-european-medicines-agency/european-federation-pharmaceutical-industries-associations-workshop-importance-dose-finding-dose_en.pdf. Accessed: 29OCT2021.
  • European Medicines Agency, Committee for Medicinal Products for Human Use. 2014. Qualification opinion of MCP-Mod as an efficient statistical methodology for model-based design and analysis of phase 2 dose finding studies under model uncertainty. https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/qualification-opinion-mcp-mod-efficient-statistical-methodology-model-based-design-analysis-phase-ii_en.pdf. Accessed: 29OCT2021.
  • Food and Drug Administration. 2015. Request for qualification of MCP-Mod as an efficient statistical methodology for model-based design and analysis of phase II dose finding studies under model uncertainty. https://www.fda.gov/media/99313/download. Accessed: 29OCT2021.
  • Food and Drug Administration, 2019. Adaptive designs for clinical trials of drugs and biologics guidance for industry. https://www.fda.gov/media/78495/download. Accessed: 29OCT2021.
  • Fu, H., and D. Manner. 2010. Bayesian adaptive dose-finding studies with delayed responses. Journal of Biopharmaceutical Statistics 20 (5):1055–1070. doi:10.1080/10543400903315740.
  • Galbraith, S., and I. C. Marschner. 2003. Interim analysis of continuous long-term endpoints in clinical trials with longitudinal outcomes. Statistics in Medicine 22 (11):1787–1805. doi:10.1002/sim.1311.
  • Garthwaite, P. H., and E. Mubwandarikwa. 2010. Selection of weights for weighted model averaging. Australian & New Zealand Journal of Statistics 52 (4):363–382. doi:10.1111/j.1467-842X.2010.00589.x.
  • Gould, A. L. 2019. BMA-Mod: A Bayesian model averaging strategy for determining dose-response relationships in the presence of model uncertainty. Biometrical Journal 61 (5):1141–1159. doi:10.1002/bimj.201700211.
  • Green, P. J. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82 (4):711–732. doi:10.1093/biomet/82.4.711.
  • Han, C., and B. P. Carlin. 2001. Markov chain Monte Carlo methods for computing Bayes factors: A comparative review. Journal of the American Statistical Association 96 (455):1122–1132. doi:10.1198/016214501753208780.
  • Hartford, A., M. Thomann, X. Chen, E. Miller, A. Bedding, S. Jorgens, L. Liu, L. Chen, and C. Morgan. 2020. Adaptive designs: Results of 2016 survey on perception and use. Therapeutic Innovation & Regulatory Science 54 (1):42–54. doi:10.1007/s43441-019-00028-y.
  • Kullback, S., and R. A. Leibler. 1951. On information and sufficiency. Annals of Mathematical Statistics 22 (1):79–86. doi:10.1214/aoms/1177729694.
  • Landau, W. M. 2021. The targets r package: A dynamic make-like function-oriented pipeline toolkit for reproducibility and high-performance computing. Journal of Open Source Software 6 (57):2959. doi:10.21105/joss.02959.
  • Marso, S. P., E. Hardy, J. Han, H. Wang, and R. J. Chilton. 2018. Changes in heart rate associated with exenatide once weekly: Pooled analysis of clinical data in patients with type 2 diabetes. Diabetes Therapy 9 (2):551–564. doi:10.1007/s13300-018-0370-z.
  • Pajor, A. 2017. Estimating the marginal likelihood using the arithmetic mean identity. Bayesian Analysis 12 (1):261–287. doi:10.1214/16-BA1001.
  • Payne, R. D. 2021. dreamer: Dose Response Models for Bayesian Model Averaging. R package version 3.0.0. https://cran.r-project.org/package=dreamer
  • Pinheiro, J., B. Bornkamp, E. Glimm, and F. Bretz. 2014. Model-based dose finding under model uncertainty using general parametric models. Statistics in Medicine 33 (10):1646–1661. doi:10.1002/sim.6052.
  • Pinheiro, J. C., F. Bretz, and M. Branson. 2006. Analysis of dose–response studies—modeling approaches. In Dose finding in drug development, pp. 146–171. Springer.
  • Qu, Y., Z. Liu, H. Fu, S. Sethuraman, and P. M. Kulkarni. 2019. Modeling the impact of preplanned dose titration on delayed response. Journal of Biopharmaceutical Statistics 29 (2):287–305. doi:10.1080/10543406.2018.1535499.
  • Sacks, L. V., H. H. Shamsuddin, Y. I. Yasinskaya, K. Bouri, M. L. Lanthier, and R. E. Sherman. 2014. Scientific and regulatory reasons for delay and denial of FDA approval of initial applications for new drugs, 2000-2012. JAMA 311 (4):378–384. doi:10.1001/jama.2013.282542.
  • Schorning, K., B. Bornkamp, F. Bretz, and H. Dette. 2016. Model selection versus model averaging in dose finding studies. Statistics in Medicine 35 (22):4021–4040. doi:10.1002/sim.6991.
  • Tan, M. H., A. Baksi, B. Krahulec, P. Kubalski, A. Stankiewicz, R. Urquhart, G. Edwards, and D. Johns. 2005. Comparison of pioglitazone and gliclazide in sustaining glycemic control over 2 years in patients with type 2 diabetes. Diabetes Care 28 (3):544–550. doi:10.2337/diacare.28.3.544.
  • Ting, N. 2006. Dose finding in drug development. New York, NY, USA: Springer Science & Business Media.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.