Adaptive Design of Personalized Dose-Finding Clinical Trials

References

• Agarwal A, Hsu D, Kale S, Langford J, Li L, Schapire R (2014) Taming the monster: A fast and simple algorithm for contextual bandits. Proc. 31st Internat. Conf. Machine Learn. 32(2):1638–1646.Google Scholar
• Ahuja V, Birge JR (2016) Response-adaptive designs for clinical trials: Simultaneous learning from multiple patients. Eur. J. Oper. Res. 248(2):619–633.Crossref, Google Scholar
• Alban A, Chick SE, Zoumpoulis S (2021) A value of information approach to designing sequential clinical trials for precision medicine. Accessed November 30, 2021, https://cattendee.abstractsonline.com/meeting/10390/presentation/6533.Google Scholar
• Albert JH, Chib S (1993) Bayesian analysis of binary and polychotomous response data. J. Amer. Statist. Assoc. 88(422):669–679.Crossref, Google Scholar
• Bastani H, Bayati M (2020) Online decision making with high-dimensional covariates. Oper. Res. 68(1):276–294.Link, Google Scholar
• Bastani H, Bayati M, Khosravi K (2021) Mostly exploration-free algorithms for contextual bandits. Management Sci. 67(3):1329–1349.Link, Google Scholar
• Bates S (2010) Progress toward personalized medicine. Drug Discovery Today 15(3-4):115–120.Crossref, Google Scholar
• Berry DA (2012) Adaptive clinical trials in oncology. Nature Rev. Clinical Oncology 9(4):199–207.Crossref, Google Scholar
• Berry DA, Herbst RS, Rubin EH (2012) Reports from the 2010 clinical and translational cancer research think tank meeting: Design strategies for personalized therapy trials. Clinical Cancer Res. 18(3):638–644.Crossref, Google 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. Carlin B, Carriquiry A, Gatsonis C, Gelman A, Kass RE, Verdinelli I, West M, eds. Case Studies in Bayesian Statistics, Lecture Notes in Statistics, vol. 162 (Springer, New York), 99–181.Google Scholar
• Berry SM, Carlin BP, Lee JJ, Muller P (2010) Bayesian Adaptive Methods for Clinical Trials (CRC Press, Boca Raton, FL).Crossref, Google Scholar
• Bertsimas D, Korolko N, Weinstein AM (2019) Covariate-adaptive optimization in online clinical trials. Oper. Res. 67(4):1150–1161.Abstract, Google Scholar
• Bhat N, Farias VF, Moallemi CC, Sinha D (2019) Near optimal A-B testing. Management Sci. 66(10):4477–4495.Link, Google Scholar
• Chick SE, Gans N, Yapar O (2021) Bayesian sequential learning for clinical trials of multiple correlated medical interventions. Management Sci., ePub ahead of print November 5, https://doi.org/10.1287/mnsc.2021.4137.Google Scholar
• Delshad S, Khademi A (2020) Information theory for ranking and selection. Naval Res. Logist. 67(4):239–253.Crossref, 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.Crossref, Google Scholar
• Ding L, Hong LJ, Shen H, Zhang X (2022) Knowledge gradient for selection with covariates: Consistency and computation. Naval Res. Logist. 69(3):496–507.Crossref, Google Scholar
• 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 II dose finding studies under model uncertainty. Report No. EMA/CHMP/SAWP/757052/2013, European Medicines Agency. 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.Google Scholar
• European Network for Health Technology Assessment (2013) Endpoints Used for Relative Effectiveness Assessment of Pharmaceuticals: Health-Related Quality of Life and Utility Measures. Report, EUnetHTA, Diemen, Netherlands.Google Scholar
• Fisher RA (1949) The Design of Experiments, 5th ed. (Oliver and Boyd, London).Google Scholar
• Frazier PI, Powell WB, Dayanik S (2008) A knowledge-gradient policy for sequential information collection. SIAM J. Control Optim. 47(5):2410–2439.Crossref, Google Scholar
• Goldenshluger A, Zeevi A (2013) A linear response bandit problem. Stochastic Systems 3(1):230–261.Link, Google Scholar
• Guo B, Yuan Y (2017) Bayesian phase I/II biomarker-based dose finding for precision medicine with molecularly targeted agents. J. Amer. Statist. Assoc. 112(518):508–520.Crossref, Google Scholar
• Hayden E (2015) California unveils ‘precision-medicine’ project. Nature, https://doi.org/10.1038/nature.2015.17324.Google Scholar
• Ildstad ST, Evans CH Jr, eds. (2001) Small Clinical Trials: Issues and Challenges (National Academies Press, Washington, DC).Google Scholar
• Janiaud P, Serghiou S, Ioannidis JP (2019) New clinical trial designs in the era of precision medicine: An overview of definitions, strengths, weaknesses, and current use in oncology. Cancer Treatment Rev. 73:20–30.Crossref, Google Scholar
• Julious SA (2004) Sample sizes for clinical trials with normal data. Statist. Medicine 23(12):1921–1986.Crossref, Google 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.Crossref, Google Scholar
• Lin R, Thall PF, Yuan Y (2019) An adaptive trial design to optimize dose-schedule regimes with delayed outcomes. Biometrics 76(1):304–315.Crossref, Google Scholar
• Liu F, Walters SJ, Julious SA (2017) Design considerations and analysis planning of a phase 2a proof of concept study in rheumatoid arthritis in the presence of possible non-monotonicity. BMC Medical Res. Methodology 17(1):1–14.Crossref, Google Scholar
• Liu S, Guo B, Yuan Y (2018) A Bayesian phase I/II trial design for immunotherapy. J. Amer. Statist. Assoc. 113(523):1016–1027.Crossref, Google Scholar
• Maxfield K, Zineh I (2021) Precision dosing: A clinical and public health imperative. J. Amer. Medical Assoc. 325(15):1505–1506.Google Scholar
• Nasrollahzadeh AA, Khademi A (2020) Optimal stopping of adaptive dose-finding trials. Service Sci. 12(2-3):80–99.Link, Google Scholar
• Nasrollahzadeh AA, Khademi A (2022) Dynamic programming for response-adaptive dose-finding clinical trials. INFORMS J. Comput. 34(2):1176–1190.Link, Google Scholar
• Negoescu DM, Frazier PI, Powell WB (2011) The knowledge-gradient algorithm for sequencing experiments in drug discovery. INFORMS J. Comput. 23(3):346–363.Link, Google Scholar
• O’Quigley J, Shen LZ, Gamst A (1999) Two-sample continual reassessment method. J. Biopharmaceutical Statist. 9(1):17–44.Crossref, Google Scholar
• Peck RW (2018) Precision medicine is not just genomics: The right dose for every patient. Annual Rev. Pharmacology Toxicology 58:105–122.Crossref, Google Scholar
• Peck RW (2021) Precision dosing: An industry perspective. Clinical Pharmacology Therapeutics 109(1):47–50.Crossref, Google Scholar
• Pharmacogenomics Knowledgebase (2021a) Clinical annotation for rs61742245 (VKORC1); Warfarin; (level 2a dosage). Accessed November 30, 2021, https://www.pharmgkb.org/gene/PA133787052/clinicalAnnotation/1183703748.Google Scholar
• Pharmacogenomics Knowledgebase (2021b) Pharmacogenetics and pharmacogenomics knowledge base downloads. Accessed November 30, 2021, https://www.pharmgkb.org/downloads.Google Scholar
• Rojas-Cordova AC, Bish EK, Hosseinichimeh N (2020) Decision-making in sequential adaptive clinical trials, with implications for drug misclassification and resource allocation. Smith A, ed. Women in Industrial and Systems Engineering (Springer, Cham, Switzerland), 321–345.Crossref, Google Scholar
• Russo D, Van Roy B (2014) Learning to optimize via posterior sampling. Math. Oper. Res. 39(4):1221–1243.Link, Google Scholar
• Russo D, Van Roy B (2018) Learning to optimize via information–directed sampling. Oper. Res. 66(1):230–252.Link, Google Scholar
• Russo DJ, Van Roy B, Kazerouni A, Osband I, Wen Z (2018) A tutorial on Thompson sampling. Foundations Trends Machine Learn. 11(1):1–96.Crossref, Google Scholar
• Ryzhov IO (2016) On the convergence rates of expected improvement methods. Oper. Res. 64(6):1515–1528.Link, Google Scholar
• Ryzhov IO, Powell WB, Frazier PI (2012) The knowledge gradient algorithm for a general class of online learning problems. Oper. Res. 60(1):180–195.Link, Google Scholar
• Schork NJ (2015) Personalized medicine: Time for one-person trials. Nature 520(7549):609–611.Crossref, Google Scholar
• U.S. Food and Drug Administration (FDA) (2019) Adaptive designs for clinical trials of drugs and biologics guidance for industry. Report No. FDA-2018-D-3124, Center for Biologics Evaluation and Research & Center for Drug Evaluation and Research. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/adaptive-design-clinical-trials-drugs-and-biologics-guidance-industry.Google Scholar
• U.S. National Library of Medicine—National Institutes of Health (2021) Clinicaltrials.gov advanced search. Accessed November 30, 2021, https://www.clinicaltrials.gov/ct2/search/advanced.Google Scholar
• Wald A (1943) On the efficient design of statistical investigations. Ann. Math. Statist. 14(2):134–140.Crossref, Google Scholar
• Wang Y, Powell WB (2018) Finite-time analysis for the knowledge-gradient policy. SIAM J. Control Optim. 56(2):1105–1129.Crossref, Google Scholar
• White PJ (2010) Patient factors that influence warfarin dose response. J. Pharmacy Practice 23(3):194–204.Crossref, Google Scholar
• Williamson SF, Jacko P, Villar SS, Jaki T (2017) A Bayesian adaptive design for clinical trials in rare diseases. Comput. Statist. Data Anal. 113:136–153.Crossref, Google Scholar
• Wu CJ, Hamada MS (2011) Experiments: Planning, Analysis, and Optimization, vol. 552 (John Wiley & Sons, Hoboken, NJ).Google Scholar