616
Views
2
CrossRef citations to date
0
Altmetric
Statistical Issues and Challenges in Clinical Trials for COVID-19 Treatments, Vaccines, Medical Devices and Diagnostics

A Sequential Predictive Power Design for a COVID Vaccine Trial

ORCID Icon, , &
Pages 42-51 | Received 15 Nov 2020, Accepted 06 Sep 2021, Published online: 15 Nov 2021

References

  • Amrhein, V., Greenland, S., and McShane, B. (2019), “Scientists Rise Up Against Statistical Significance,” Nature 567, 305–307.
  • Baden, L. R., et al. (2021), “Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine,” New England Journal of Medicine, 384, 403–416.
  • Dmitrienko, A., and Wang, M. D. (2006), “Bayesian Predictive Approach to Interim Monitoring in Clinical Trials,” Statistics in Medicine, 25, 2178–2195.
  • Evans, M., and Moshonov, H. (2006), “Checking for Prior-Data Conflict,” Bayesian Analysis, 1, 893–914.
  • Food and Drug Administration (FDA), (2020), “Development and Licensure of Vaccines to Prevent COVID-19: Guidance for Industry,” available at https://www.fda.gov/regulatory-information/search-fda-guidance-documents/development-andlicensure-vaccines-prevent-covid-19.
  • Gordon Lan, K. K., and DeMets, D. L. (1983), “Discrete Sequential Boundaries for Clinical Trials,” Biometrika, 70, 659–663.
  • Gsponer, T., Gerber, F., Bornkamp, B., Ohlssen, D., Vandemeule- broecke, M., and Schmidli, H. (2014), “A Practical Guide to Bayesian Group Sequential Designs,” Pharmaceutical Statistics, 13, 71–80.
  • Harhay, M. O., and Donaldson, G. C. (2020), “Guidance on Statistical Reporting to Help Improve Your Chances of a Favorable Statistical Review,” in American Journal of Respiratory and Critical Care Medicine, 201, 1035–1038.
  • Mehta, C., and Tsiatis, A. (2001), “Flexible Sample Size Considerations Using Information-Based Interim Monitoring,” Drug Information Journal - Drug Information Portal 35, 1095–1112.
  • Netea, M. G., Giamarellos-Bourboulis, E. J., Domínguez-Andrés, J., Curtis, N., Crevel, R. van, Veerdonk, F. L. van de, and Bonten, M. (2020), “Trained Immunity: A Tool for Reducing Susceptibility to and the Severity of SARS-CoV-2 Infection,” Cell 181, 969–977.
  • O’Neill, L. A. J., and Netea, M. G. (2020), “BCG-Induced Trained Immunity: Can It Offer Protection Against COVID-19?” Nature Reviews Immunology, 20, 335–337.
  • Polack, F. P., Thomas, S. J., Kitchin, N., Absalon, J., Gurtman, A., Lockhart, S., Perez, J. L., Pérez Marc, G., Moreira, E. D., Zerbini, C., Bailey, R., Swanson, K. A., Roychoudhury, S., Koury, K., Li, P., Kalina, W. V., Cooper, D., Frenck, R. W., Hammitt, L. L., Türeci, Ö., Nell, H., Schaefer, A., Ünal, S., Tresnan, D. B., Mather, S., Dormitzer, P. R.,? ahin, U., Jansen, K. U., Gruber, W. C., Aberg, J., Addo, M., Akhan, S., Albertson, T., Al-Ibrahim, M., Alt?n, S., Anderson, C., Andrews, C., Arora, S., Balik, I., Barnett, E., Bauer, G., Baumann-Noss, S., Berhe, M., Bradley, P., Brandon, D., Brune, D., Burgher, A., Butcher, B., Butuk, D., Cannon, K., Cardona, J., Cavelli, R., Chalhoub, F., Christensen, S., Christensen, T., Chu, L., Cox, S., Crook, G., Davis, M., Davit, R., Denham, D., Dever, M., Donskey, C., Doust, M., Dunn, M., Earl, J., Eder, F., Eich, A., Ensz, D., Essink, B., Falcone, R., Falsey, A., Farrington, C., Finberg, R., Finn, D., Fitz-Patrick, D., Fortmann, S., Fouche, L., Fragoso, V., Frenck, R., Fried, D., Fuller, G., Fussell, S., Garcia-Diaz, J., Gentry, A., Glover, R., Greenbaum, C., Grubb, S., Hammitt, L., Harper, C., Harper, W., Hartman, A., Heller, R., Hendrix, E., Herrington, D., Jennings, T., Karabay, O., Kaster, S., Katzman, S., Kingsley, J., Klein, N., Klein, T., Koch, M., Köksal, I., Koren, M., Kutner, M., Lee, M., Leibowitz, M., Levin, M., Libster, R., Lillestol, M., Lucasti, C., Luttermann, M., Manning, M. B. E., Martin, M., Matherne, P., McMurray, J., Mert, A., Middleton, R., Mitha, E., Morawski, E., Moreira, E., Mulligan, M., Murray, A., Mussaji, M., Musungaie, D., Nell, H., Odekirk, L., Ogbuagu, O., Paolino, K., Patel, S., Peterson, J., Pickrell, P., Polack, F., Poretz, D., Raad, G., Randall, W., Rankin, B., Reynolds, Riesenberg, R., Rodriguez, H., Rosen, J., Rubino, J., Rupp, R., Saiger, S., Salata, R., Saleh, J., Schaefer, A., Schear, M., Schultz, A., Schwartz, H., Segall, N., Seger, W., Senders, S., Sharp, S., Shoffner, S., Simsek Yavuz, S., Sligh, T., Smith, W., Stacey, H., Stephens, M., Studdard, H., Tabak, F., Talaat, K., Thomas, S., Towner, W., Tran, V., Ünal, S., Usdan, L., Vanchiere, J., Varano, S., Wadsworth, E. T. L., Walsh, W., Walter, E., Wappner, D., Whiles, R., Williams, H., Wilson, J., Winkle, P., Winokur, P., Wolf, T., Yozviak, J., Zerbini, C. (2020), “Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine,” New England Journal of Medicine, 383, 2603–2615.
  • Psioda, M. A., and Ibrahim, J. G. (2019), “Bayesian Clinical Trial Design Using Historical Data That Inform the Treatment Effect,” Biostatistics 20, 400–415.
  • Saville, B. R., Connor, J. T., Ayers, G. D., and Alvarez, J. (2014), “The Utility of Bayesian Predictive Probabilities for Interim Monitoring of Clinical Trials,” Clinical Trials, 11, 485–493.
  • Singh, S., Maurya, R. P., and Singh, R. K. (2020), “Trained Immunity” From Mycobacterium spp. Exposure or BCG Vaccination and COVID-19 Outcomes,” PLoS Pathog 16, e1008969.
  • Spiegelhalter, D. J., Abrams, K. R., and Myles, J. P. (2004), Bayesian Approaches to Clinical Trials and Health-Care Evaluation, Chichester: Wiley. Available at: https://cds.cern.ch/record/997022.
  • Walker, L. E., and Nieto-Barajas, S. G. (2002), “Markov Beta and Gamma Processes for Modelling Hazard Rates,” Scandinavian Journal of Statistics, 29, 413–424.
  • Wang, H., Rosner, G. L., and Goodman, S. N. (2016), “Quantifying Over-Estimation in Early Stopped Clinical Trials and the “Freezing Effect” on Subsequent Research” Clinical Trials, 13, 621–631.
  • Wasserstein, R. L., Schirm, A. L., and Lazar, N. A. (2019), “Moving to a World Beyond ‘p < 0.05’,” The American Statistician 73, 1–19.
  • Woodcock, J., and LaVange, L. M. (2017), “Master Protocols to Study Multiple Therapies, Multiple Diseases, or Both,” New England Journal of Medicine, 1, 62–70.
  • Zhu, H., and Yu, Q. (2017), “A Bayesian Sequential Design Using Alpha Spending Function to Control Type I Error,” Statistical Methods in Medical Research, 26, 2184–2196.

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.