Bayesian interim analysis for prospective randomized studies: reanalysis of the acute myeloid leukemia HOVON 132 clinical trial.
Journal
Blood cancer journal
ISSN: 2044-5385
Titre abrégé: Blood Cancer J
Pays: United States
ID NLM: 101568469
Informations de publication
Date de publication:
27 Mar 2024
27 Mar 2024
Historique:
received:
23
01
2024
accepted:
08
03
2024
revised:
06
03
2024
medline:
28
3
2024
pubmed:
28
3
2024
entrez:
28
3
2024
Statut:
epublish
Résumé
Randomized controlled trials (RCTs) are the gold standard to establish the benefit-risk ratio of novel drugs. However, the evaluation of mature results often takes many years. We hypothesized that the addition of Bayesian inference methods at interim analysis time points might accelerate and enforce the knowledge that such trials may generate. In order to test that hypothesis, we retrospectively applied a Bayesian approach to the HOVON 132 trial, in which 800 newly diagnosed AML patients aged 18 to 65 years were randomly assigned to a "7 + 3" induction with or without lenalidomide. Five years after the first patient was recruited, the trial was negative for its primary endpoint with no difference in event-free survival (EFS) between experimental and control groups (hazard ratio [HR] 0.99, p = 0.96) in the final conventional analysis. We retrospectively simulated interim analyses after the inclusion of 150, 300, 450, and 600 patients using a Bayesian methodology to detect early lack of efficacy signals. The HR for EFS comparing the lenalidomide arm with the control treatment arm was 1.21 (95% CI 0.81-1.69), 1.05 (95% CI 0.86-1.30), 1.00 (95% CI 0.84-1.19), and 1.02 (95% CI 0.87-1.19) at interim analysis 1, 2, 3 and 4, respectively. Complete remission rates were lower in the lenalidomide arm, and early deaths more frequent. A Bayesian approach identified that the probability of a clinically relevant benefit for EFS (HR < 0.76, as assumed in the statistical analysis plan) was very low at the first interim analysis (1.2%, 0.6%, 0.4%, and 0.1%, respectively). Similar observations were made for low probabilities of any benefit regarding CR. Therefore, Bayesian analysis significantly adds to conventional methods applied for interim analysis and may thereby accelerate the performance and completion of phase III trials.
Identifiants
pubmed: 38538587
doi: 10.1038/s41408-024-01037-3
pii: 10.1038/s41408-024-01037-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
56Informations de copyright
© 2024. The Author(s).
Références
Goring S, Taylor A, Müller K, Li TJJ, Korol EE, Levy AR, et al. Characteristics of non-randomised studies using comparisons with external controls submitted for regulatory approval in the USA and Europe: a systematic review. BMJ Open. 2019;9:e024895.
doi: 10.1136/bmjopen-2018-024895
pubmed: 30819708
pmcid: 6398650
Hatswell AJ, Baio G, Berlin JA, Irs A, Freemantle N. Regulatory approval of pharmaceuticals without a randomised controlled study: analysis of EMA and FDA approvals 1999-2014. BMJ Open. 2016;6:e011666.
doi: 10.1136/bmjopen-2016-011666
pubmed: 27363818
pmcid: 4932294
Fashoyin-Aje LA, Mehta GU, Beaver JA, Pazdur R. The on- and off-ramps of oncology accelerated approval. N Engl J Med. 2022;387:1439–42.
doi: 10.1056/NEJMp2208954
pubmed: 36129992
Grimes DA, Schulz KF. An overview of clinical research: the lay of the land. Lancet. 2002;359:57–61.
doi: 10.1016/S0140-6736(02)07283-5
pubmed: 11809203
Moher D, Hopewell S, Schulz KF, Montori V, Gøtzsche PC, Devereaux PJ, et al. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ. 2010;340:c869.
doi: 10.1136/bmj.c869
pubmed: 20332511
pmcid: 2844943
Pignatti F, Wilking U, Wilking N, Delgado J, Bergh J. The value of anticancer drugs—a regulatory view. Nat Rev Clin Oncol. 2021;19:207–15.
doi: 10.1038/s41571-021-00584-z
pubmed: 34873312
Marini BL, Goodman AM, Perissinotti AJ. The essential role of randomised controlled trials. Lancet Haematol. 2023;10:e486–e487.
doi: 10.1016/S2352-3026(23)00130-8
pubmed: 37407137
van Rosmalen J, Dejardin D, van Norden Y, Löwenberg B, Lesaffre E. Including historical data in the analysis of clinical trials: Is it worth the effort? Stat Methods Med Res. 2018;27:3167–82.
doi: 10.1177/0962280217694506
pubmed: 28322129
Hobbs BP, Sargent DJ, Carlin BP. Commensurate priors for incorporating historical information in clinical trials using general and generalized linear models. Bayesian Anal. 2012;7:639–74.
doi: 10.1214/12-BA722
pubmed: 24795786
pmcid: 4007051
Hobbs BP, Carlin BP, Mandrekar SJ, Sargent DJ. Hierarchical commensurate and power prior models for adaptive incorporation of historical information in clinical trials. Biometrics. 2011;67:1047–56.
doi: 10.1111/j.1541-0420.2011.01564.x
pubmed: 21361892
pmcid: 3134568
Cheung YK, Chappell R. Sequential designs for phase I clinical trials with late-onset toxicities. Biometrics. 2000;56:1177–82.
doi: 10.1111/j.0006-341X.2000.01177.x
pubmed: 11129476
Babb JS, Rogatko A. Patient specific dosing in a cancer phase I clinical trial. Stat Med. 2001;20:2079–90.
doi: 10.1002/sim.848
pubmed: 11439422
Ji Y, Liu P, Li Y, Bekele BN. A modified toxicity probability interval method for dose-finding trials. Clin Trials. 2010;7:653–63.
doi: 10.1177/1740774510382799
pubmed: 20935021
pmcid: 5038924
Yuan Y, Hess KR, Hilsenbeck SG, Gilbert MR. Bayesian optimal interval design: a simple and well-performing design for phase I oncology trials. Clin Cancer Res. 2016;22:4291–301.
doi: 10.1158/1078-0432.CCR-16-0592
pubmed: 27407096
pmcid: 5047439
Angus DC, Berry S, Lewis RJ, Al-Beidh F, Arabi Y, van Bentum-Puijk W, et al. The REMAP-CAP (randomized embedded multifactorial adaptive platform for community-acquired pneumonia) study. rationale and design. Ann Am Thorac Soc. 2020;17:879–91.
doi: 10.1513/AnnalsATS.202003-192SD
pubmed: 32267771
pmcid: 7328186
Schuetze SM, Wathen JK, Lucas DR, Choy E, Samuels BL, Staddon AP, et al. SARC009: Phase 2 study of dasatinib in patients with previously treated, high-grade, advanced sarcoma. Cancer. 2016;122:868–74.
doi: 10.1002/cncr.29858
pubmed: 26710211
Hirakawa A, Nishikawa T, Yonemori K, Shibata T, Nakamura K, Ando M, et al. Utility of Bayesian single-arm design in new drug application for rare cancers in Japan: a case study of phase 2 trial for sarcoma. Ther Innov Regul Sci. 2018;52:334–8.
doi: 10.1177/2168479017728989
pubmed: 29714533
Kim ES, Herbst RS, Wistuba II, Lee JJ, Blumenschein GR Jr, Tsao A, et al. The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov. 2011;1:44–53.
doi: 10.1158/2159-8274.CD-10-0010
pubmed: 22586319
pmcid: 4211116
Papadimitrakopoulou V, Lee JJ, Wistuba II, Tsao AS, Fossella FV, Kalhor N, et al. The BATTLE-2 study: a biomarker-integrated targeted therapy study in previously treated patients with advanced non-small-cell lung cancer. J Clin Oncol. 2016;34:3638–47.
doi: 10.1200/JCO.2015.66.0084
pubmed: 27480147
pmcid: 5065110
Berry DA, Dhadda S, Kanekiyo M, Li D, Swanson CJ, Irizarry M, et al. Lecanemab for patients with early Alzheimer disease: bayesian analysis of a phase 2b dose-finding randomized clinical trial. JAMA Netw Open. 2023;6:e237230.
doi: 10.1001/jamanetworkopen.2023.7230
pubmed: 37040116
pmcid: 10091161
Broglio K, Meurer WJ, Durkalski V, Pauls Q, Connor J, Berry D, et al. Comparison of Bayesian vs frequentist adaptive trial design in the stroke hyperglycemia insulin network effort trial. JAMA Netw Open. 2022;5:e2211616.
doi: 10.1001/jamanetworkopen.2022.11616
pubmed: 35544137
pmcid: 9096598
Muss HB, Berry DA, Cirrincione CT, Theodoulou M, Mauer AM, Kornblith AB, et al. Adjuvant chemotherapy in older women with early-stage breast cancer. N Engl J Med. 2009;360:2055–65.
doi: 10.1056/NEJMoa0810266
pubmed: 19439741
pmcid: 3082436
Reis G, Silva EASM, Silva DCM, Thabane L, Milagres AC, Ferreira TS, et al. Effect of early treatment with ivermectin among patients with covid-19. N Engl J Med. 2022;386:1721–31.
doi: 10.1056/NEJMoa2115869
pubmed: 35353979
Takahashi T, Yamanaka T, Seto T, Harada H, Nokihara H, Saka H, et al. Prophylactic cranial irradiation versus observation in patients with extensive-disease small-cell lung cancer: a multicentre, randomised, open-label, phase 3 trial. Lancet Oncol. 2017;18:663–71.
doi: 10.1016/S1470-2045(17)30230-9
pubmed: 28343976
Nogueira RG, Jadhav AP, Haussen DC, Bonafe A, Budzik RF, Bhuva P, et al. Thrombectomy 6 to 24 h after Stroke with a mismatch between deficit and infarct. N Engl J Med. 2018;378:11–21.
doi: 10.1056/NEJMoa1706442
pubmed: 29129157
Reardon MJ, Van Mieghem NM, Popma JJ, Kleiman NS, Søndergaard L, Mumtaz M, et al. Surgical or transcatheter aortic-valve replacement in intermediate-risk patients. N Engl J Med. 2017;376:1321–31.
doi: 10.1056/NEJMoa1700456
pubmed: 28304219
Shah PL, Slebos D-J, Cardoso PFG, Cetti E, Voelker K, Levine B, et al. Bronchoscopic lung-volume reduction with Exhale airway stents for emphysema (EASE trial): randomised, sham-controlled, multicentre trial. Lancet. 2011;378:997–1005.
doi: 10.1016/S0140-6736(11)61050-7
pubmed: 21907863
Ferreira D, Vivot A, Diemunsch P, Meyer N. Bayesian analysis from phase III trials was underused and poorly reported: a systematic review. J Clin Epidemiol. 2020;123:107–13.
doi: 10.1016/j.jclinepi.2020.03.021
pubmed: 32259583
Löwenberg B, Pabst T, Maertens J, Gradowska P, Biemond BJ, Spertini O, et al. Addition of lenalidomide to intensive treatment in younger and middle-aged adults with newly diagnosed AML: the HOVON-SAKK-132 trial. Blood Adv. 2021;5:1110–21.
doi: 10.1182/bloodadvances.2020003855
pubmed: 33616652
pmcid: 7903238
Löwenberg B, Pabst T, Maertens J, van Norden Y, Biemond BJ, Schouten HC, et al. Therapeutic value of clofarabine in younger and middle-aged (18-65 years) adults with newly diagnosed AML. Blood. 2017;129:1636–45.
doi: 10.1182/blood-2016-10-740613
pubmed: 28049642
Döhner H, Estey E, Grimwade D, Amadori S, Appelbaum FR, Büchner T, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;129:424–47.
doi: 10.1182/blood-2016-08-733196
pubmed: 27895058
pmcid: 5291965
Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res. 2011;46:399–424.
doi: 10.1080/00273171.2011.568786
Plummer M rjags: Bayesian Graphical Models using MCMC R package version 4-12 (2021). https://CRAN.R-project.org/package=rjags .
R Core Team. R: A language and environment for statistical computing (2023). R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ .
East 6. Statistical software for the design, simulation and monitoring clinical trials. Cambridge MA: Cytel Inc.; 2020.
Kumar A, Soares H, Djulbegovic B. Are statistically non-significant findings necessarily negative? a review of all phase III randomized controlled trials in hematology conducted by NCI sponsored cooperative groups. Blood. 2005;106:293.
doi: 10.1182/blood.V106.11.293.293
Qi H, Rizopoulos D, van Rosmalen J. Sample size calculation for clinical trials analyzed with the meta-analytic-predictive approach. Res Synth Methods. 2023;14:396–413.
doi: 10.1002/jrsm.1618
pubmed: 36625478
Center for Drug Evaluation, Research. Adaptive Design Clinical Trials for Drugs and Biologics Guidance for Industry. U.S. Food and Drug Administration. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/adaptive-design-clinical-trials-drugs-and-biologics-guidance-industry (Accessed 20 Dec 2022).
Muehlemann N, Zhou T, Mukherjee R, Hossain MI, Roychoudhury S, Russek-Cohen E. A tutorial on modern Bayesian methods in clinical trials. Ther Innov Regul Sci. 2023;57:402–16.
doi: 10.1007/s43441-023-00515-3
pubmed: 37081374
pmcid: 10117244
Lewis JH, Kilgore ML, Goldman DP, Trimble EL, Kaplan R, Montello MJ, et al. Participation of patients 65 years of age or older in cancer clinical trials. J Clin Oncol. 2003;21:1383–9.
doi: 10.1200/JCO.2003.08.010
pubmed: 12663731
Ruiter R, Burggraaf J, Rissmann R. Under-representation of elderly in clinical trials: an analysis of the initial approval documents in the Food and Drug Administration database. Br J Clin Pharm. 2019;85:838–44.
doi: 10.1111/bcp.13876
Makady A, de Boer A, Hillege H, Klungel O, Goettsch W. What is real-world data? a review of definitions based on literature and stakeholder interviews. Value Health. 2017;20:858–65.
doi: 10.1016/j.jval.2017.03.008
pubmed: 28712614
Hermans S, van Norden Y, van Werkhoven E, Dinmohamed A, Huijgens P, Ossenkoppele G, et al. Real-world data as supplementary controls for the prospective randomized hovon-103 trial in intensively treated elderly acute myeloid leukemia patients. Hemasphere. 2023;7:e323641c.