1. Home
  2. Issues
  3. Volume 1, Issue 3 (2023)
  4. Seamless Clinical Trials with Doubly Ada ...

The New England Journal of Statistics in Data Science


Seamless Clinical Trials with Doubly Adaptive Biased Coin Designs
Volume 1, Issue 3 (2023), pp. 314–322
Pub. online: 1 March 2023      Type: Methodology Article      Open accessOpen Access
Area: Biomedical Research
Accepted
29 January 2023
Published
1 March 2023

Abstract

In addition to scientific questions, clinical trialists often explore or require other design features, such as increasing the power while controlling the type I error rate, minimizing unnecessary exposure to inferior treatments, and comparing multiple treatments in one clinical trial. We propose implementing adaptive seamless design (ASD) with response adaptive randomization (RAR) to satisfy various clinical trials’ design objectives. However, the combination of ASD and RAR poses a challenge in controlling the type I error rate. In this paper, we investigated how to utilize the advantages of the two adaptive methods and control the type I error rate. We offered the theoretical foundation for this procedure. Numerical studies demonstrated that our methods could achieve efficient and ethical objectives while controlling the type I error rate.

References

[1] 
Andersen, J. S. (1996). Clinical trial designs—made to order. Journal of Biopharmaceutical Statistics 6(4) 515–522. https://doi.org/10.1198/016214503000000576. MR2011680
[2] 
Antognini, A. B. and Giovagnoli, A. (2010). Compound optimal allocation for individual and collective ethics in binary clinical trials. Biometrika 97(4) 935–946. https://doi.org/10.1214/aos/1079120137. MR2051008
[3] 
Atkinson, A. C. and Biswas, A. (2005). Bayesian Adaptive Biased-Coin Designs for Clinical Trials with Normal Responses. Biometrics 61(1) 118–125. https://doi.org/10.1214/08-AOS655. MR2543702
[4] 
Barnes, P. J., Pocock, S. J., Magnussen, H., Iqbal, A., Kramer, B., Higgins, M. and Lawrence, D. (2010). Integrating indacaterol dose selection in a clinical study in COPD using an adaptive seamless design. Pulmonary Pharmacology and Therapeutics 23(3) 165–171.
[5] 
Bauer, P. and Köhne, K. (1994). Evaluation of experiments with adaptive interim analyses. Biometrics 50(4) 1029–1041. https://doi.org/10.1002/sim.3578. MR2675244
[6] 
Berry, D. A. (2004). Bayesian statistics and the efficiency and ethics of clinical trials. Statistical Science 19(1) 175–187. https://doi.org/10.1111/j.0006-341X.2002.00823.x. MR1945019
[7] 
Berry, D. A. (2012). Adaptive clinical trials in oncology. Nature Reviews Clinical Oncology 9(4) 199–207.
[8] 
Biswas, A., Mandal, S. and Bhattacharya, R. (2011). Multi-treatment optimal response-adaptive designs for phase III clinical trials. Journal of the Korean Statistical Society 40(1) 33–44. https://doi.org/10.1081/BIP-200062857. MR2190575
[9] 
Bowden, J. and Glimm, E. (2008). Unbiased estimation of selected treatment means in two-stage trials. Biometrical Journal 50(4) 515–527. https://doi.org/10.1002/sim.5757. MR3073825
[10] 
Bowden, J. and Glimm, E. (2014). Conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials. Biometrical Journal 56(2) 332–349. https://doi.org/10.1002/sim.4430. MR2880482
[11] 
Brannath, W., Gutjahr, G. and Bauer, P. (2012). Probabilistic foundation of confirmatory adaptive designs. Journal of the American Statistical Association 107(498) 824–832.
[12] 
Brannath, W., Zuber, E., Branson, M., Bretz, F., Gallo, P., Posch, M. and Racine-Poon, A. (2009). Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology. Statistics in Medicine 28(10) 1445–1463. https://doi.org/10.1198/016214502388618852. MR1951257
[13] 
Brannath, W., Koenig, F. and Bauer, P. (2007). Multiplicity and flexibility in clinical trials. Pharmaceutical Statistics: The Journal of Applied Statistics in the Pharmaceutical Industry 6(3) 205–216. https://doi.org/10.1093/biomet/ass002. MR2931269
[14] 
Bretz, F., Schmidli, H., König, F., Racine, A. and Maurer, W. (2006). Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: General concepts. Biometrical Journal 48(4) 623–634. https://doi.org/10.1093/biomet/63.3.655. MR0468056
[15] 
Bretz, F., Koenig, F., Brannath, W., Glimm, E. and Posch, M. (2009). Adaptive designs for confirmatory clinical trials. Statistics in Medicine 28(8) 1181–1217. https://doi.org/10.1002/sim.2389. MR2221962
[16] 
Chen, Y. J., Gesser, R. and Luxembourg, A. (2015). A seamless phase IIB/III adaptive outcome trial: design rationale and implementation challenges. Clinical Trials 12(1) 84–90.
[17] 
Chow, S. C. and Chang, M. (2012) Adaptive Design Methods in Clinical Trials, Second Edition. Chapman and Hall/CRC. https://doi.org/10.1002/sim.6974. MR3545616
[18] 
Chow, S. C., Lu, Q. and Tse, S. K. (2007). Statistical analysis for two-stage seamless design with different study endpoints. Journal of Biopharmaceutical Statistics 17(6) 1163–1176. https://doi.org/10.1111/j.0006-341X.2001.00909.x. MR1863454
[19] 
Cohen, A. and Sackrowitz, H. B. (1989). Two stage conditionally unbiased estimators of the selected mean. Statistics & Probability Letters 8(3) 273–278.
[20] 
Dunnett, C. W. (1955). A multiple comparison procedure for comparing several treatments with a control. Journal of the American Statistical Association 50(272) 1096–1121. https://doi.org/10.1002/bimj.200410119. MR2145117
[21] 
Gao, L., Zhu, H. and Zhang, L. (2020). Sequential monitoring of response-adaptive randomized clinical trials with sample size re-estimation. Journal of Statistical Planning and Inference 205 129–137. https://doi.org/10.1093/biomet/77.3.507. MR1087840
[22] 
Hampson, L. V. and Jennison, C. (2015). Optimizing the data combination rule for seamless phase II/III clinical trials. Statistics in Medicine 34(1) 39–58. https://doi.org/10.1002/bimj.200510231. MR2247049
[23] 
Hu, F. and Rosenberger, W. F. (2006) The Theory of Response-Adaptive Randomization in Clinical Trials. John Wiley and Sons, Inc., New York.
[24] 
Hu, F. and Rosenberger, W. F. (2003). Optimality, variability, power: evaluating response-adaptive randomization procedures for treatment comparisons. Journal of the American Statistical Association 98(463) 671–678. https://doi.org/10.1093/biomet/73.3.751. MR0897872
[25] 
Hu, F. and Zhang, L.-X. (2004). Asymptotic properties of doubly adaptive biased coin designs for multitreatment clinical trials. The Annals of Statistics 32(1) 268–301. https://doi.org/10.1002/sim.3863. MR2756565
[26] 
Hu, F., Zhang, L.-X. and He, X. (2009). Efficient randomized-adaptive designs. The Annals of Statistics 2543–2560. https://doi.org/10.1002/sim.3436. MR2522318
[27] 
Hu, F., Hu, Y., Ma, W., Zhang, L. and Zhu, H. (2015). Statistical inference of adaptive randomized clinical trials for personalized medicine. Clinical Investigation 5(4) 415–425.
[28] 
Huang, X., Ning, J., Li, Y., Estey, E., Issa, J. P. and Berry, D. A. (2009). Using short-term response information to facilitate adaptive randomization for survival clinical trials. Statistics in Medicine 28(12) 1680–1689. https://doi.org/10.1016/j.jspi.2004.05.006. MR2200477
[29] 
Inoue, L. Y., Thall, P. F. and Berry, D. A. (2002). Seamlessly expanding a randomized phase II trial to phase III. Biometrics 58(4) 823–831. https://doi.org/10.1016/j.jspi.2007.05.045. MR2427293
[30] 
Ivanova, A. and Rosenberger, W. F. (2000). A comparison of urn designs for randomized clinical trials of K > 2 treatments. Journal of Biopharmaceutical Statistics 10(1) 93–107.
[31] 
Kelly, P. J., Stallard, N. and Todd, S. (2005). An adaptive group sequential design for phase II/III clinical trials that select a single treatment from several. Journal of Biopharmaceutical Statistics 15(4) 641–658.
[32] 
Kimani, P. K., Todd, S. and Stallard, N. (2013). Conditionally unbiased estimation in phase II/III clinical trials with early stopping for futility. Statistics in Medicine 32(17) 2893–2910.
[33] 
Koopmeiners, J. S., Feng, Z. and Pepe, M. S. (2012). Conditional estimation after a two-stage diagnostic biomarker study that allows early termination for futility. Statistics in Medicine 31(5) 420–435. https://doi.org/10.2307/2531495. MR1010517
[34] 
Lehmacher, W. and Wassmer, G. (1999). Adaptive sample size calculations in group sequential trials. Biometrics 55(4) 1286–1290.
[35] 
Liu, Q., Proschan, M. A. and Pledger, G. W. (2002). A unified theory of two-stage adaptive designs. Journal of the American Statistical Association 97(460) 1034–1041. https://doi.org/10.1080/03610929908832376. MR1707106
[36] 
Magirr, D., Jaki, T. and Whitehead, J. (2012). A generalized Dunnett test for multi-arm multi-stage clinical studies with treatment selection. Biometrika 99(2) 494–501. https://doi.org/10.1198/016214506000000906. MR2345540
[37] 
Marcus, R., Eric, P. and Gabriel, K. R. (1976). On closed testing procedures with special reference to ordered analysis of variance. Biometrika 63(3) 655–660.
[38] 
Posch, M., Koenig, F., Branson, M., Brannath, W., Dunger-Baldauf, C. and Bauer, P. (2005). Testing and estimation in flexible group sequential designs with adaptive treatment selection. Statistics in Medicine 24(24) 3697–3714.
[39] 
Prowell, T. M., Theoret, M. R. and Pazdur, R. (2016). Seamless oncology-drug development. New England Journal of Medicine 374(21) 2001–2003. https://doi.org/10.1016/j.jspi.2016.09.002. MR3574510
[40] 
Robertson, D. S., Prevost, A. T. and Bowden, J. (2016). Unbiased estimation in seamless phase II/III trials with unequal treatment effect variances and hypothesis-driven selection rules. Statistics in Medicine 35(22) 3907–3922. https://doi.org/10.1177/0962280214550759. MR3592738
[41] 
Rosenberger, W. F., Stallard, N., Ivanova, A., Harper, C. N. and Ricks, M. L. (2001). Optimal adaptive designs for binary response trials. Biometrics 57(3) 909–913.
[42] 
Rout, C., Rocke, D., Levin, J., Gouws, E. and Reddy, D. (1993). A reevaluation of the role of crystalloid preload in the prevention of hypotension associated with spinal anesthesia for elective cesarean section. Anesthesiology 79(2) 262–269. https://doi.org/10.1002/cjs.11609. MR4349636
[43] 
Sampson, A. R. and Sill, M. W. (2005). Drop-the-losers design: Normal case. Biometrical Journal 47(3) 257–268. https://doi.org/10.1002/sim.4218. MR2828933
[44] 
Schaid, D. J., Wieand, S. A. M. and Therneau, T. M. (1990). Optimal two-stage screening designs for survival comparisons. Biometrika 77(3) 507–513.
[45] 
Schmidli, H., Bretz, F., Racine, A. and Maurer, W. (2006). Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: Applications and practical considerations. Biometrical Journal 48(4) 635–643.
[46] 
Shen, L. (2001). An improved method of evaluating drug effect in a multiple dose clinical trial. Statistics in Medicine 20(13) 1913–1929. https://doi.org/10.1214/105051605000000746. MR2209345
[47] 
Simes, R. J. (1986). An improved Bonferroni procedure for multiple tests of significance. Biometrika 73(3) 751–754. https://doi.org/10.1016/j.jspi.2008.11.003. MR2508003
[48] 
Stallard, N. (2010). A confirmatory seamless phase II/III clinical trial design incorporating short-term endpoint information. Statistics in Medicine 29(9) 959–971. https://doi.org/10.1214/10-AOS796. MR2676888
[49] 
Stallard, N. and Friede, T. (2008). A group-sequential design for clinical trials with treatment selection. Statistics in Medicine 27(29) 6209–6227. https://doi.org/10.1002/cjs.11140. MR2968398
[50] 
Stallard, N. and Todd, S. (2003). Sequential designs for phase III clinical trials incorporating treatment selection. Statistics in Medicine 22(5) 689–703. https://doi.org/10.1080/10543400701645322. MR2414569
[51] 
Stallard, N. and Todd, S. (2005). Point estimates and confidence regions for sequential trials involving selection. Journal of Statistical Planning and Inference 135(2) 402–419.
[52] 
Stallard, N., Todd, S. and Whitehead, J. (2008). Estimation following selection of the largest of two normal means. Journal of Statistical Planning and Inference 138(6) 1629–1638.
[53] 
Tamura, R. N., Faries, D. E., Andersen, J. S. and Heiligenstein, J. H. (1994). A case study of an adaptive clinical trial in the treatment of out-patients with depressive disorder. Journal of the American Statistical Association 89(427) 768–776.
[54] 
Thall, P. F. and Wathen, J. K. (2007). Practical Bayesian adaptive randomisation in clinical trials. European Journal of Cancer 43(5) 859–866.
[55] 
Thall, P. F., Simon, R. and Ellenberg, S. S. (1988). Two-stage selection and testing designs for comparative clinical trials. Biometrika 75(2) 303–310.
[56] 
Thall, P. F., Simon, R. and Ellenberg, S. S. (1989). A two-stage design for choosing among several experimental treatments and a control in clinical trials. Biometrics 45 537–547.
[57] 
Todd, S. and Stallard, N. (2005). A new clinical trial design combining phases 2 and 3: Sequential designs with treatment selection and a change of endpoint. Drug Information Journal 39(2) 109–118.
[58] 
Troendle, J. F. and Yu, K. F. (1999). Conditional estimation following a group sequential clinical trial. Communications in Statistics - Theory and Methods 28(7) 1617–1634.
[59] 
Tymofyeyev, Y., Rosenberger, W. F. and Hu, F. (2007). Implementing optimal allocation in sequential binary response experiments. Journal of the American Statistical Association 102(477) 224–234.
[60] 
US Food and Drug Administration (2006). Critical Path Opportunities Report and List.
[61] 
US Food and Drug Administration (2018). Expansion cohorts: Use in first-in-human clinical trials to expedite development of oncology drugs and biologics guidance for industry.
[62] 
Wang, L., Chen, Y. and Zhu, H. (2017). Implementing optimal allocation in clinical trials with multiple endpoints. Journal of Statistical Planning and Inference 182 88–99.
[63] 
Wason, J., Stallard, N., Bowden, J. and Jennison, C. (2017). A multi-stage drop-the-losers design for multi-arm clinical trials. Statistical Methods in Medical Research 26(1) 508–524.
[64] 
Wei, L. and Durham, S. (1978). The randomized play-the-winner rule in medical trials. Journal of the American Statistical Association 73(364) 840–843.
[65] 
Yu, J. and Lai, D. (2021). Interim analysis of sequential estimation-adjusted urn models with sample size re-estimation. Canadian Journal of Statistics 49(4) 1075–1092.
[66] 
Yuan, Y., Huang, X. and Liu, S. (2011). A Bayesian response-adaptive covariate-balanced randomization design with application to a leukemia clinical trial. Statistics in Medicine 30(11) 1218–1229.
[67] 
Zang, Y. and Lee, J. J. (2014). Adaptive clinical trial designs in oncology. Chinese Clinical Oncology 3(4).
[68] 
Zeymer, U., Suryapranata, H., Monassier, J. P., Opolski, G., Davies, J., Rasmanis, G., Linssen, G., Tebbe, U., Schroder, R., Tiemann, R., Machnig, T., and Neuhaus, K. L. (2001). The Na+/H+ exchange inhibitor Eniporide as an adjunct to early reperfusion therapy for acute myocardial infarction. Results of the evaluation of the safety and cardioprotective effects of Eniporide in acute myocardial infarction (ESCAM1) trial. J Am Coll Cardiol 38 1644–1650.
[69] 
Zhang, L.-X., Hu, F. and Cheung, S. H. (2006). Asymptotic theorems of sequential estimation-adjusted urn models. The Annals of Applied Probability 16(1) 340–369.
[70] 
Zhu, H. and Hu, F. (2009). Implementing optimal allocation for sequential continuous responses with multiple treatments. Journal of Statistical Planning and Inference 139(7) 2420–2430.
[71] 
Zhu, H. and Hu, F. (2010). Sequential monitoring of response-adaptive randomized clinical trials. Annals of Statistics 38(4) 2218–2241.
[72] 
Zhu, H. and Hu, F. (2012). Interim analysis of clinical trials based on urn models. Canadian Journal of Statistics 40(3) 550–568.
[73] 
Zhu, H., Piao, J., Lee, J. J., Hu, F. and Zhang, L. (2020). Response adaptive randomization procedures in seamless phase II/III clinical trials. Journal of Biopharmaceutical Statistics 30(1) 3–17.

Full article Related articles PDF XML
Full article Related articles PDF XML

Copyright
© 2023 New England Statistical Society
Open access article under the CC BY license.

Keywords
Adaptive design Ethics Efficiency Response adaptive randomizations Type I error

Metrics
since December 2021
477

Article info
views

374

Full article
views

262

PDF
downloads

111

XML
downloads


Share


RSS