Comparative Effectiveness Research using Bayesian Adaptive Designs for Rare Diseases: Response Adaptive Randomization Reusing Participants

References

• Barohn, R., Gajewski, B., Pasnoor, M., Brown, A., Herbelin, L., Kimminau, K., Mudaranthakam, D., Jawdat, O., Dimachkie, M., and PAIN-CONTRoLS Study Team. (2020), “Patient Assisted Intervention for Neuropathy: Comparison of Treatment in Real Life Situations (PAIN-CONTRoLS): Bayesian Adaptive Comparative Effectiveness Trial,” JAMA Neurology, 78. DOI: 10.1001/jamaneurol.2020.2590.
   Web of Science ®Google Scholar
• Berry, S. M., Carlin, B. P., Lee, J. J., Muller, P.(2011), Bayesian Adaptive Methods for Clinical Trials, Boca Raton: Chapman & Hall/CRC Press.
   Google Scholar
• Betancourt, M.(2016), “A Conceptual Introduction to Hamiltonian Monte Carlo,” eprint arXiv:1701.02434,
   Google Scholar
• Brown, A. R., Gajewski, B. J., Aaronson, L. S., Mudaranthakam, D. P., Hunt, S. L., Berry, S. M., Quintana, M., Pasnoor, M., Dimachkie, M. M., Jawdat, O., Herbelin, L., and Barohn, R. J. (2016), “A Bayesian Comparative Effectiveness Trial in Action: Developing a Platform for Multisite Study Adaptive Randomization,” Trials, 17, 428. DOI: 10.1186/s13063-016-1544-5.
   PubMed Web of Science ®Google Scholar
• Burckhardt, C. S., and Jones, K. D. (2003), “Adult Measures of Pain: The McGill Pain Questionnaire (MPQ), Rheumatoid Arthritis Pain Scale (RAPS), Short-Form McGill Pain Questionnaire (SF-MPQ), Verbal Descriptive Scale (VDS), Visual Analog Scale (VAS), and West Haven-Yale Multidisciplinary Pain Inventory (WHYMPI),” Arthritis Care & Research, 49, S96–S104. DOI: 10.1002/art.11440.
   Google Scholar
• Freidlin, B., Korn, E. L. (2013), “Adaptive Randomization Versus Interim Monitoring,” Journal of Clinical Oncology, 31, 969–970. DOI: 10.1200/JCO.2012.45.0254.
   PubMed Web of Science ®Google Scholar
• Gajewski, B. J., Berry, S. M., Quintana, M., Pasnoor, M., Dimachkie, M., Herbelin, L., Barohn, R. (2015), “Building Efficient Comparative Effectiveness Trials Through Adaptive Designs, Utility Functions, and Accrual Rate Optimization: Finding the Sweet Spot,” Statistics in Medicine, 34, 1134–1149. DOI: 10.1002/sim.6403.
   PubMed Web of Science ®Google Scholar
• Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A., and Rubin, D. (2014), Bayesian Data Analysis, New York: CRC Press.
   Google Scholar
• Jang, Y., Zhao, W., and Durkalsk-Mauldin, V. (2017), “Impact of Adaption Algorithm, Timing, and Stopping Boundaries on the Performance of Bayesian Response Adaptive Randomization in Confirmative Trials With Binary End-Point,” Contempary Clinical Trials, 62, 114–120. DOI: 10.1016/j.cct.2017.08.019.
   PubMed Web of Science ®Google Scholar
• Nason, M., and Follmann, D. (2010), “Design and Analysis of Crossover Trials for Absorbing Binary Endpoints,” Biometrics, 7, 958–965. DOI: 10.1111/j.1541-0420.2009.01358.x.
   Google Scholar
• Pasnoor, M., Dimachkie, M. M., and Barohn, R. J. (2013), “Cryptogenic Sensory Polyneuropathy,” Neurologic Clinics, 31, 463–476. DOI: 10.1016/j.ncl.2013.01.008.
   PubMed Web of Science ®Google Scholar
• R Core Team (2017), R: A Language and Environment for Statistical Computing, Vienna: R Foundation for Statistical Computing.
   Google Scholar
• Rosenberger, W. F., Vidyashankar, A. N., and Agarwal, D. K.(2001), “Covariate-Adjusted Response-Adaptive Designs for Binary Response,” Journal of Biopharmaceutical Statistics, 11, 227–236. DOI: 10.1081/BIP-120008846.
   PubMedGoogle Scholar
• Stan Development Team (2017), RStan: the R interface to Stan (version 2.16.1), available at http://mc-stan.org.
   Google Scholar
• Stan Development Team (2017), Stan Modeling Language User’s Guide and Reference Manual (Version 2.16.0), available at http://mc-stan.org.
   Google Scholar
• Stensland, K. D., McBride, R. B., Latif, A., Wisnivesky, J., Hendricks, R., Roper, N. (2014), “Adult Cancer Clinical Trials That Fail to Complete: An Epidemic,” Journal of the National Cancer Institute, 106.
   PubMedGoogle Scholar
• Tang, C., Sherman, S. I., Price, M., Weng, J., Davis, S. E., Hong, D.S., Yao, J. C., Buzdar, A., Wilding, J., and Lee, J. J. (2017), “Clinical Trial Characteristics and Barriers to Participant Accrual: The MD Anderson Cancer Center Experience Over 30 Years, a Historical Foundation for Trial Improvement,” Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 23, 1414–1421. DOI: 10.1158/1078-0432.CCR-16-2439.
   PubMed Web of Science ®Google Scholar
• U.S. Department of Health and Human Services Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER). (2019), “Adaptive Designs for Clinical Trials of Drugs and Biologics Guidance for Industry,” available at https://www.fda.gov/regulatory-information/search-fda-guidance-documents/adaptive-design-clinical-trials-drugs-and-biologics-guidance-industry
   Google Scholar
• Wellek, S., and Blettner, M. (2012), “On the Proper Use of the Crossover Design in Clinical Trials: Part 18 of a Series on Evaluation of Scientific Publications,” Dtsch Arztebl Int, 109, 276–281.
   PubMed Web of Science ®Google Scholar
• Wick, J., Berry, S. M., Yeh, H., Choi, W., Pacheco, C. M., Daley, C., and Gajewski, B. J. (2017), “A Novel Evaluation of Optimality for Randomized Controlled Trials,” Journal of Biopharmaceutical Statistics, 27, 659–672. DOI: 10.1080/10543406.2016.1198367.
   PubMed Web of Science ®Google Scholar