Brookmeyer, R., Crowley, J.: A confidence interval for the median survival time. Biometrics 38(1), 29–41 (1982). https://doi.org/10.2307/2530286

Article  Google Scholar 

Choi, S.C., Pepple, P.A.: Monitoring clinical trials based on predictive probability of significance. Biometrics 45(1), 317–323 (1989). https://doi.org/10.2307/2532056

Article  CAS  Google Scholar 

Choi, S.C., Smith, P.J., Becker, D.P.: Early decision in clinical trials when the treatment differences are small: experience of a controlled trial in head trauma. Control. Clin. Trials 6(4), 280–288 (1985). https://doi.org/10.1016/0197-2456(85)90104-7

Article  CAS  Google Scholar 

Chuang-Stein, C.: Sample size and the probability of a successful trial. Pharm. Stat. 5(4), 305–309 (2006). https://doi.org/10.1002/pst.232

Article  Google Scholar 

CODA: collaborative: a randomized trial comparing antibiotics with appendectomy for appendicitis. N. Engl. J. Med. 383(20), 1907–1919 (2020). https://doi.org/10.1056/NEJMoa2014320

Article  Google Scholar 

Dallow, N., Fina, P.: The perils with the misuse of predictive power. Pharm. Stat. 10(4), 311–317 (2011). https://doi.org/10.1002/pst.467

Article  Google Scholar 

Demets, D.L., Lan, K.G.: Interim analysis: the alpha spending function approach. Stat. Med. 13(13–14), 1341–1352 (1994). https://doi.org/10.1002/sim.4780131308

Article  CAS  Google Scholar 

Dmitrienko, A., Wang, M.D.: Bayesian predictive approach to interim monitoring in clinical trials. Stat. Med. 25(13), 2178–2195 (2006). https://doi.org/10.1002/sim.2204

Article  Google Scholar 

Fenaux, P., Platzbecker, U., Mufti, G.J., et al.: Luspatercept in patients with lower-risk myelodysplastic syndromes. N. Engl. J. Med. 382(2), 140–151 (2020). https://doi.org/10.1056/NEJMoa1908892

Article  CAS  Google Scholar 

Gillett, R.: An average power criterion for sample size estimation. J. R. Stat. Soc. Ser D (Statistician) 43(3), 389–394 (1994). https://doi.org/10.2307/2348574

Article  Google Scholar 

Halperin, M., Lan, K.G., Ware, J.H., Johnson, N.J., DeMets, D.L.: An aid to data monitoring in long-term clinical trials. Control. Clin. Trials. 3(4), 311–323 (1982). https://doi.org/10.1016/0197-2456(82)90022-8

Article  CAS  Google Scholar 

Jennison, C., Turnbull, B.W.: Group sequential methods with applications to clinical trials. CRC Press, Boca Raton (1999). https://doi.org/10.1201/9780367805326

Book  Google Scholar 

Johns, D., Andersen, J.S.: Use of predictive probabilities in phase II and phase III clinical trials. J. Biopharm. Stat. 9(1), 67–79 (1999). https://doi.org/10.1081/BIP-100101000

Article  CAS  Google Scholar 

Klein, J.P., Van Houwelingen, H.C., Ibrahim, J.G., Scheike, T.H. (eds.): Handbook of survival analysis. CRC Press, Boca Raton (2014). https://doi.org/10.1201/b16248

Book  Google Scholar 

Kundu, M. G.: Package ‘LongCART’. https://cran.r-project.org/web/packages/LongCART/LongCART (2022)

Lachin, J.M.: A review of methods for futility stopping based on conditional power. Stat. Med. 24(18), 2747–2764 (2005). https://doi.org/10.1002/sim.2151

Article  Google Scholar 

Lan, K.G., Wittes, J.: The B-value: a tool for monitoring data. Biometrics 44(2), 579–585 (1988). https://doi.org/10.2307/2531870

Article  CAS  Google Scholar 

Lan, K.G., Hu, P., Proschan, M.A.: A conditional power approach to the evaluation of predictive power. Stat. Biopharm. Res. 1(2), 131–136 (2009). https://doi.org/10.1198/sbr.2009.0035

Article  Google Scholar 

Lassman, A., Pugh, S., Wang, T., et al.: Depatuxizumab mafodotin in EGFR-amplified newly diagnosed glioblastoma: a phase III randomized clinical trial. Neuro-Oncol. (2022). https://doi.org/10.1093/neuonc/noac173

Article  Google Scholar 

Lee, P.M.: Bayesian Statistics: An Introduction, 4th edn. Wiley, New York (2014)

Google Scholar 

Matulonis, U.A., Oaknin, A., Pignata, S., et al.: Mirvetuximab soravtansine (MIRV) in patients with platinum-resistant ovarian cancer with high folate receptor alpha (FR\(\alpha\)) expression: Characterization of antitumor activity in the SORAYA study. J. Clin. Oncol. 40(16-suppl), 5512–5512 (2022). https://doi.org/10.1200/JCO.2022.40.16_suppl.5512

Article  Google Scholar 

Miller, S.J.: Supplemental chapter: order statistics and the median theorem. In: Probability Lifesaver.Princeton University Press, Princeton, NJ (2016) http://web.williams.edu/Mathematics/sjmiller/public_html/probabilitylifesaver/supplementalchap_mediantheoremandorderstatistics. Accessed 14 Sept. 2021

Moore, K.N., Oza, A.M., Colombo, N., et al.: Phase III, randomized trial of mirvetuximab soravtansine versus chemotherapy in patients with platinum-resistant ovarian cancer: primary analysis of FORWARD I. Ann. Oncol. 32(6), 757–765 (2021). https://doi.org/10.1016/j.annonc.2021.02.017

Article  CAS  Google Scholar 

O’Hagan, A., Stevens, J.W., Campbell, M.J.: Assurance in clinical trial design. Pharm. Stat. 4(3), 187–201 (2005). https://doi.org/10.1002/pst.175

Article  Google Scholar 

Owzar, K., Jung, S.H.: Designing phase II studies in cancer with time-to-event endpoints. Clin. Trials 5(3), 209–221 (2008). https://doi.org/10.1177/1740774508091748

Article  Google Scholar 

Parmar, M.K., Torri, V., Stewart, L.: Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat. Med. 17(24), 2815–2834 (1998). https://doi.org/10.1002/(SICI)1097-0258(19981230)17:24<2815::AID-SIM110>3.0.CO;2-8

Article  CAS  Google Scholar 

Proschan, M.A., Lan, K.G., Wittes, J.T.: Statistical monitoring of clinical trials: a unified approach. Springer, New York (2006). https://doi.org/10.1007/978-0-387-44970-8

Book  Google Scholar 

Rufibach, K., Burger, H.U., Abt, M.: Bayesian predictive power: choice of prior and some recommendations for its use as probability of success in drug development. Pharm. Stat. 15(5), 438–446 (2016). https://doi.org/10.1002/pst.1764

Article  Google Scholar 

Saville, B.R., Connor, J.T., Ayers, G.D., Alvarez, J.: The utility of Bayesian predictive probabilities for interim monitoring of clinical trials. Clin. Trials 11(4), 485–493 (2014). https://doi.org/10.1177/1740774514531352

Article  Google Scholar 

Spiegelhalter, D.J., Freedman, L.S.: A predictive approach to selecting the size of a clinical trial, based on subjective clinical opinion. Stat. Med. 5(1), 1–13 (1986). https://doi.org/10.1002/sim.4780050103

Article  CAS  Google Scholar 

Spiegelhalter, D.J., Freedman, L.S., Blackburn, P.R.: Monitoring clinical trials: conditional or predictive power? Control. Clin. Trials 7(1), 8–17 (1986). https://doi.org/10.1016/0197-2456(86)90003-6

Article  CAS  Google Scholar 

Spiegelhalter, D.J., Abrams, K.R., Myles, J.P.: Bayesian approaches to clinical trials and health-care evaluation, vol. 13. Wiley, New York (2004). https://doi.org/10.1002/0470092602

Book  Google Scholar 

Tang, Z.: Optimal futility interim design: a predictive probability of success approach with time-to-event endpoint. J. Biopharm. Stat. 25(6), 1312–1319 (2015). https://doi.org/10.1080/10543406.2014.983646

Article  Google Scholar 

Van Den Bent, M., Eoli, M., Sepulveda, J.M., et al.: INTELLANCE 2/EORTC 1410 randomized phase II study of Depatux-M alone and with temozolomide vs temozolomide or lomustine in recurrent EGFR amplified glioblastoma. Neuro-Oncol. 22(5), 684–693 (2020). https://doi.org/10.1093/neuonc/noaa115

Article  Google Scholar 

Wang, Y., Fu, H., Kulkarni, P., Kaiser, C.: Evaluating and utilizing probability of study success in clinical development. Clin. Trials 10(3), 407–413 (2013). https://doi.org/10.1177/1740774513478229

Article  Google Scholar