Dynamic Programming for Response-Adaptive Dose-Finding Clinical Trials

Published Online:https://doi.org/10.1287/ijoc.2021.1082

References

  • Ahuja V, Birge JR (2016) Response-adaptive designs for clinical trials: Simultaneous learning from multiple patients. Eur. J. Oper. Res. 248(2):619–633.CrossrefGoogle Scholar
  • Berger MP, Wong WK (2009) An Introduction to Optimal Designs for Social and Biomedical Research, vol. 83 (John Wiley & Sons, New York).CrossrefGoogle Scholar
  • Berry DA, Mueller P, Grieve AP, Smith M, Parke T, Blazek R, Mitchard N, Krams M (2002) Adaptive Bayesian designs for dose-ranging drug trials. Gatsonis C, Kass RE, Carlin B, Carriquiry A, Gelman A, Verdinelli I, West M, eds. Case Studies in Bayesian Statistics, Lecture Notes in Statistics, vol. 162 (Springer, New York), 99–181.Google Scholar
  • Carlin BP, Berry SM, Lee JJ, Muller P (2010) Bayesian Adaptive Methods for Clinical Trials (CRC Press, Boca Raton, FL).Google Scholar
  • Chick SE, Forster M, Pertile P (2017) A Bayesian decision theoretic model of sequential experimentation with delayed response. J. Roy. Statist. Soc. Ser. B: Statist. Methodology 79(5):1439–1462.CrossrefGoogle Scholar
  • Chow SC, Pong A (2016) Handbook of Adaptive Designs in Pharmaceutical and Clinical Development (CRC Press, Boca Raton, FL).Google Scholar
  • Dette H, Bretz F, Pepelyshev A, Pinheiro J (2008) Optimal designs for dose-finding studies. J. Amer. Statist. Assoc. 103(483):1225–1237.CrossrefGoogle Scholar
  • Eli Lilly and Company (2018) A study of LY2951742 in participants with mild to moderate osteoarthritis knee pain. Accessed October 1, 2020, https://clinicaltrials.gov/ct2/show/results/NCT02192190.Google Scholar
  • Frazier PI, Powell WB (2011) Consistency of sequential Bayesian sampling policies. SIAM J. Control Optim. 49(2):712–731.CrossrefGoogle Scholar
  • Frazier PI, Powell WB, Dayanik S (2008) A knowledge-gradient policy for sequential information collection. SIAM J. Control Optim. 47(5):2410–2439.CrossrefGoogle Scholar
  • Frazier P, Powell W, Dayanik S (2009) The knowledge-gradient policy for correlated normal beliefs. INFORMS J. Comput. 21(4):599–613.LinkGoogle Scholar
  • Frühwirth-Schnatter S (1994) Data augmentation and dynamic linear models. J. Time Ser. Anal. 15(2):183–202.CrossrefGoogle Scholar
  • Gadagkar SR, Call GB (2015) Computational tools for fitting the Hill equation to dose–response curves. J. Pharmacological Toxicological Methods 71:68–76.CrossrefGoogle Scholar
  • Griffin R, Lebovitz Y, English R, eds. (2010) Transforming Clinical Research in the United States: Challenges and Opportunities: Workshop Summary (National Academies Press, Washington, DC).Google Scholar
  • Gupta SS, Miescke KJ (1996) Bayesian look ahead one-stage sampling allocations for selection of the best population. J. Statist. Planning Inference 54(2):229–244.CrossrefGoogle Scholar
  • Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J (2014) Clinical development success rates for investigational drugs. Nature Biotechnology 32(1):40–51.CrossrefGoogle Scholar
  • Hennessey VG, Rosner GL, Bast RC Jr, Chen MY (2010) A Bayesian approach to dose–response assessment and synergy and its application to in vitro dose–response studies. Biometrics 66(4):1275–1283.CrossrefGoogle Scholar
  • Holm Hansen C, Warner P, Parker RA, Walker BR, Critchley HO, Weir CJ (2017) Development of a Bayesian response-adaptive trial design for the Dexamethasone for excessive menstruation study. Statist. Methods Medical Res. 26(6):2681–2699.CrossrefGoogle Scholar
  • Johnstone RH, Bardenet R, Gavaghan DJ, Mirams GR (2016) Hierarchical Bayesian inference for ion channel screening dose-response data. Wellcome Open Res. 1–6.Google Scholar
  • Kotas J, Ghate A (2016) Response-guided dosing for rheumatoid arthritis. IIE Trans. Healthcare Systems Engrg. 6(1):1–21.CrossrefGoogle Scholar
  • Kotas J, Ghate A (2018) Bayesian learning of dose–response parameters from a cohort under response-guided dosing. Eur. J. Oper. Res. 265(1):328–343.CrossrefGoogle Scholar
  • Krams M, Lees KR, Hacke W, Grieve AP, Orgogozo JM, Ford GA, (2003) Acute stroke therapy by inhibition of neutrophils (ASTIN): An adaptive dose-response study of UK-279, 276 in acute ischemic stroke. Stroke 34(11):2543–2548.CrossrefGoogle Scholar
  • Lenz RA, Pritchett YL, Berry SM, Llano DA, Han S, Berry DA, Sadowsky CH, Abi-Saab WM, Saltarelli MD (2015) Adaptive dose-finding phase 2 trial evaluating the safety and efficacy of ABT-089 in mild to moderate Alzheimer disease. Alzheimer Disease Associated Disorders 29(3):192–199.CrossrefGoogle Scholar
  • Müller P, Berry DA, Grieve AP, Krams M (2006) A Bayesian decision-theoretic dose-finding trial. Decision Anal. 3(4):197–207.LinkGoogle Scholar
  • Nasrollahzadeh AA, Khademi A (2020) Optimal stopping of adaptive dose-finding trials. Service Sci. 12(2-3):80–99.LinkGoogle Scholar
  • National Institutes of Health (2014) Notice of revised NIH definition of clinical trial. Accessed October 1, 2020, http://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-015.html.Google Scholar
  • O’Quigley J, Iasonos A, Bornkamp B (2017) Handbook of Methods for Designing, Monitoring, and Analyzing Dose-Finding Trials (CRC Press, Boca Raton, FL).CrossrefGoogle Scholar
  • Parizi MS, Ghate A (2016) Lot-sizing in sequential auctions while learning bid and demand distributions. 2016 Winter Simulation Conf. (WSC) (IEEE, Piscataway, NJ), 895–906.Google Scholar
  • Powell WB, Ryzhov IO (2012) Optimal Learning, vol. 841 (John Wiley & Sons, New York).CrossrefGoogle Scholar
  • Press WH (2009) Bandit solutions provide unified ethical models for randomized clinical trials and comparative effectiveness research. Proc. Natl. Acad. Sci. USA. 106(52):22387–22392.CrossrefGoogle Scholar
  • Rosenberger WF (1996) New directions in adaptive designs. Statist. Sci. 11(2):137–149.CrossrefGoogle Scholar
  • Smith MK, Jones I, Morris MF, Grieve AP, Tan K (2006) Implementation of a Bayesian adaptive design in a proof of concept study. Pharmaceutical. Statist. 5(1):39–50.CrossrefGoogle Scholar
  • Snapinn S, Chen MG, Jiang Q, Koutsoukos T (2006) Assessment of futility in clinical trials. Pharmaceutical Statist.: J. Appl. Statist. Pharmaceutical Industry 5(4):273–281.Google Scholar
  • The European Medicines Agency (2014) Qualification opinion of MCP-Mod as an efficient statistical methodology for model-based design and analysis of Phase II dose finding studies under model uncertainty. Accessed October 1, 2020, https://www.ema.europa.eu/en/committees/committee-medicinal-products-human-use-chmp.Google Scholar
  • Tufts (2014) Cost to develop and win marketing approval for a new drug is $2.6 billion. Accessed October 1, 2020, http://csdd.tufts.edu/news/complete_story/pr_tufts_csdd_2014_cost_study.Google Scholar
  • U.S. Food and Drug Administration (2017) The drug development process: Clinical research. Accessed October 1, 2020, https://www.fda.gov/ForPatients/Approvals/Drugs/ucm405622.htm.Google Scholar
  • U.S. Food and Drug Administration (2018) Adaptive designs for clinical trials of drugs and biologics: Draft guidance for industry. Accessed October 1, 2020, https://www.fda.gov/downloads/drugs/guidances/ucm201790.pdf.Google Scholar
  • Villar SS, Bowden J, Wason J (2015) Multi-armed bandit models for the optimal design of clinical trials: Benefits and challenges. Statist. Sci. 30(2):199–215.CrossrefGoogle Scholar
  • Villar SS, Rosenberger WF (2018) Covariate-adjusted response-adaptive randomization for multi-arm clinical trials using a modified forward looking Gittins index rule. Biometrics 74(1):49–57.CrossrefGoogle Scholar
  • Wang Y, Wang C, Powell W (2016) The knowledge gradient for sequential decision making with stochastic binary feedbacks. Lawrence N, Reid M, eds. Internat. Conf. Machine Learn. (PMLR, New York), 1138–1147.Google Scholar
  • Weir CJ, Spiegelhalter DJ, Grieve AP (2007) Flexible design and efficient implementation of adaptive dose-finding studies. J. Biopharmaceutical Statist. 17(6):1033–1050.CrossrefGoogle Scholar
  • West MJ, Harrison PJ (1997) Bayesian Forecasting and Dynamic Models (Springer-Verlag, New York).Google Scholar
  • Yin G, Chen N, Jack Lee J (2012) Phase II trial design with Bayesian adaptive randomization and predictive probability. J. Roy. Statist. Soc. Ser. C: Appl. Statist. 61(2):219–235.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.