Eight predictive powers with historical and interim data for futility and efficacy analysis

References

• Chen, M. H., Ibrahim, J. G., Lam, P., Yu, A., & Zhang, Y. Y. (2011). Bayesian design of noninferiority trials for medical devices using historical data. Biometrics, 67(3), 1163–1170. https://doi.org/10.1111/biom.2011.67.issue-3
   PubMed Web of Science ®Google Scholar
• Choi, S. C., Smith, P. J., & Becker, D. P. (1985). Early decision in clinical trials when the treatment differences are small. Controlled Clinical Trials, 6(4), 280–288. https://doi.org/10.1016/0197-2456(85)90104-7
   Google Scholar
• Chuang-Stein, C. (2006). Sample size and the probability of a successful trial. Pharmaceutical Statistics, 5(4), 305–309. https://doi.org/10.1002/(ISSN)1539-1612
   PubMed Web of Science ®Google Scholar
• Chuang-Stein, C., & Kirby, S. (2017). Quantitative decisions in drug development. Springer.
   Google Scholar
• Deng, Q. Q., Zhang, Y. Y., Roy, D., & Chen, M. H. (2020). Superiority of combining two independent trials in interim futility analysis. Statistical Methods in Medical Research, 29(2), 522–540. https://doi.org/10.1177/0962280219840383
   PubMed Web of Science ®Google Scholar
• Dignam, J. J., Bryant, J., Wieand, H. S., Fisher, B., & Wolmark, N. (1998). Early stopping of a clinical trial when there is evidence of no treatment benefit: protocol b-14 of the national surgical adjuvant breast and bowel project. Controlled Clinical Trials, 19(6), 575–588. https://doi.org/10.1016/S0197-2456(98)00041-5
   PubMedGoogle Scholar
• Dmitrienko, A., & Wang, M. D. (2006). Bayesian predictive approach to interim monitoring in clinical trials. Statistics in Medicine, 25(13), 2178–2195. https://doi.org/10.1002/(ISSN)1097-0258
   PubMed Web of Science ®Google Scholar
• Ibrahim, J. G., Chen, M. H., Lakshminarayanan, M., Liu, G. F., & Heyse, J. F. (2015). Bayesian probability of success for clinical trials using historical data. Statistics in Medicine, 34(2), 249–264. https://doi.org/10.1002/sim.v34.2
   PubMed Web of Science ®Google Scholar
• Jiang, K. (2011). Optimal sample sizes and go/no-go decisions for phase ii/iii development programs based on probability of success. Statistics in Biopharmaceutical Research, 3(3), 463–475. https://doi.org/10.1198/sbr.2011.10068
   Web of Science ®Google Scholar
• Kirby, S., Burke, J., Chuang-Stein, C., & Sin, C. (2012). Discounting phase 2 results when planning phase 3 clinical trials. Pharmaceutical Statistics, 11(5), 373–385. https://doi.org/10.1002/pst.1521
   PubMed Web of Science ®Google Scholar
• Lan, K. K. G., & Wittes, J. T. (2012). Some thoughts on sample size: a Bayesian frequentist hybrid approach. Clinical Trials, 9(5), 561–569. https://doi.org/10.1177/1740774512453784
   PubMed Web of Science ®Google Scholar
• O'Hagan, A., Stevens, J. W., & Campbell, M. J. (2005). Assurance in clinical trial design. Pharmaceutical Statistics, 4(3), 187–201. https://doi.org/10.1002/(ISSN)1539-1612
   Web of Science ®Google Scholar
• Schmidli, H., Bretz, F., & Racine-Poon, A. (2007). Bayesian predictive power for interim adaptation in seamless phase ii/iii trials where the endpoint is survival up to some specified timepoint. Statistics in Medicine, 26(27), 4925–4938. https://doi.org/10.1002/(ISSN)1097-0258
   PubMed Web of Science ®Google Scholar
• Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian approaches to clinical trials and health-care evaluation. Wiley.
   Google Scholar
• Spiegelhalter, D. J., Freedman, L. S., & Blackburn, P. R. (1986). Monitoring clinical trials: conditional or predictive power?. Controlled Clinical Trials, 7(1), 8–17. https://doi.org/10.1016/0197-2456(86)90003-6
   PubMedGoogle Scholar
• Trzaskoma, B., & Sashegyi, A. (2007). Predictive probability of success and the assessment of futility in large outcomes trials. Journal of Biopharmaceutical Statistics, 17(1), 45–63. https://doi.org/10.1080/10543400601001485
   PubMed Web of Science ®Google Scholar
• Tsiatis, A. A. (1981). The asymptotic joint distribution of the efficient scores test for the proportional hazards model calculated over time. Biometrika, 68(1), 311–315. https://doi.org/10.1093/biomet/68.1.311
   Web of Science ®Google Scholar
• Wang, S. J., Hung, H. M. J., & O'Neill, R. T. (2006). Adapting the sample size planning of a phase iii trial based on phase ii data. Pharmaceutical Statistics, 5(2), 85–97. https://doi.org/10.1002/(ISSN)1539-1612
   PubMed Web of Science ®Google Scholar
• Zhang, J., Carlin, B. P., Neaton, J. D., Soon, G. G., Nie, L., Kane, R., Virnig, B. A., & Chu, H. (2014). Network meta-analysis of randomized clinical trials: reporting the proper summaries. Clinical Trials, 11(2), 246–262. https://doi.org/10.1177/1740774513498322
   PubMed Web of Science ®Google Scholar
• Zhang, Y. Y., Rong, T. Z., & Li, M. M. (2020a). The contemplated average success probability for normally distributed models with an application to optimal sample sizes selection. Statistics in Medicine, 39(23), 3173–3183. https://doi.org/10.1002/sim.v39.23
   PubMed Web of Science ®Google Scholar
• Zhang, Y. Y., Rong, T. Z., & Li, M. M. (2020b). A new expectation identity and its application in the calculations of predictive powers assuming normality. Chinese Journal of Applied Probability and Statistics, 36(5), 523–535. https://doi.org/10.3969/j.issn.1001-4268.2020.05.007
   Google Scholar
• Zhang, Y. Y., & Ting, N. (2018). Bayesian sample size determination for a phase iii clinical trial with diluted treatment effect. Journal of Biopharmaceutical Statistics, 28(6), 1119–1142. https://doi.org/10.1080/10543406.2018.1436556
   PubMed Web of Science ®Google Scholar
• Zhang, Y. Y., & Ting, N. (2020). Sample size considerations for a phase iii clinical trial with diluted treatment effect. Statistics in Biopharmaceutical Research, 12(3), 311–321. https://doi.org/10.1080/19466315.2019.1599414
   Web of Science ®Google Scholar