Forced randomization: the what, why, and how.

Drug supply chain management Interactive response technology (IRT) Multi-center clinical trial Poisson-gamma model

Journal

BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545

Informations de publication

Date de publication:
08 Oct 2024
Historique:
received: 03 04 2024
accepted: 13 09 2024
medline: 9 10 2024
pubmed: 9 10 2024
entrez: 8 10 2024
Statut: epublish

Résumé

When running a randomized controlled trial (RCT), a clinical site may face a situation when an eligible trial participant is to be randomized to the treatment that is not available at the site. In this case, there are two options: not to enroll the participant, or, without disclosing to the site, allocate the participant to a treatment arm with drug available at the site using a built-in feature of the interactive response technology (IRT). In the latter case, one has employed a "forced randomization" (FR). There seems to be an industry-wide consensus that using FR can be acceptable in confirmatory trials provided there are "not too many" instances of forcing. A better understanding of statistical properties of FR is warranted. We described four different IRT configurations with or without FR and illustrated them using a simple example. We discussed potential merits of FR and outlined some relevant theoretical risks and risk mitigation strategies. We performed a search using Cortellis Regulatory Intelligence database (IDRAC) ( www.cortellis.com ) to understand the prevalence of FR in clinical trial practice. We also proposed a structured template for development and evaluation of randomization designs featuring FR and showcased an application of this template for a hypothetical multi-center 1:1 RCT under three experimental settings ("base case", "slower recruitment", and "faster recruitment") to explore the effect of four different IRT configurations in combination with three different drug supply/re-supply strategies on some important operating characteristics of the trial. We also supplied the Julia code that can be used to reproduce our simulation results and generate additional results under user-specified experimental scenarios. FR can eliminate refusals to randomize patients, which can cause frustration for patients and study site personnel, improve the study logistics, drug supply management, cost-efficiency, and recruitment time. Nevertheless, FR carries some potential risks that should be reviewed at the study planning stage and, ideally, prospectively addressed through risk mitigation planning. The Cortellis search identified only 9 submissions that have reported the use of FR; typically, the FR option was documented in IRT specifications. Our simulation evidence showed that under the considered realistic experimental settings, the percentage of FR is expected to be low. When FR with backfilling was used in combination with high re-supply strategy, the final treatment imbalance was negligibly small, the proportion of patients not randomized due to the lack of drug supply was close to zero, and the time to complete recruitment was shortened compared to the case when FR was not allowed. The drug overage was primarily determined by the intensity of the re-supply strategy and to a smaller extent by the presence or absence of the FR feature in IRT. FR with a carefully chosen drug supply/re-supply strategy can result in quantifiable improvements in the patients' and site personnel experience, trial logistics and efficiency while preventing an undesirable refusal to randomize a patient and a consequential unblinding at the site. FR is a useful design feature of multi-center RCTs provided it is properly planned for and carefully implemented.

Sections du résumé

BACKGROUND BACKGROUND
When running a randomized controlled trial (RCT), a clinical site may face a situation when an eligible trial participant is to be randomized to the treatment that is not available at the site. In this case, there are two options: not to enroll the participant, or, without disclosing to the site, allocate the participant to a treatment arm with drug available at the site using a built-in feature of the interactive response technology (IRT). In the latter case, one has employed a "forced randomization" (FR). There seems to be an industry-wide consensus that using FR can be acceptable in confirmatory trials provided there are "not too many" instances of forcing. A better understanding of statistical properties of FR is warranted.
METHODS METHODS
We described four different IRT configurations with or without FR and illustrated them using a simple example. We discussed potential merits of FR and outlined some relevant theoretical risks and risk mitigation strategies. We performed a search using Cortellis Regulatory Intelligence database (IDRAC) ( www.cortellis.com ) to understand the prevalence of FR in clinical trial practice. We also proposed a structured template for development and evaluation of randomization designs featuring FR and showcased an application of this template for a hypothetical multi-center 1:1 RCT under three experimental settings ("base case", "slower recruitment", and "faster recruitment") to explore the effect of four different IRT configurations in combination with three different drug supply/re-supply strategies on some important operating characteristics of the trial. We also supplied the Julia code that can be used to reproduce our simulation results and generate additional results under user-specified experimental scenarios.
RESULTS RESULTS
FR can eliminate refusals to randomize patients, which can cause frustration for patients and study site personnel, improve the study logistics, drug supply management, cost-efficiency, and recruitment time. Nevertheless, FR carries some potential risks that should be reviewed at the study planning stage and, ideally, prospectively addressed through risk mitigation planning. The Cortellis search identified only 9 submissions that have reported the use of FR; typically, the FR option was documented in IRT specifications. Our simulation evidence showed that under the considered realistic experimental settings, the percentage of FR is expected to be low. When FR with backfilling was used in combination with high re-supply strategy, the final treatment imbalance was negligibly small, the proportion of patients not randomized due to the lack of drug supply was close to zero, and the time to complete recruitment was shortened compared to the case when FR was not allowed. The drug overage was primarily determined by the intensity of the re-supply strategy and to a smaller extent by the presence or absence of the FR feature in IRT.
CONCLUSION CONCLUSIONS
FR with a carefully chosen drug supply/re-supply strategy can result in quantifiable improvements in the patients' and site personnel experience, trial logistics and efficiency while preventing an undesirable refusal to randomize a patient and a consequential unblinding at the site. FR is a useful design feature of multi-center RCTs provided it is properly planned for and carefully implemented.

Identifiants

pubmed: 39379810
doi: 10.1186/s12874-024-02340-0
pii: 10.1186/s12874-024-02340-0
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

234

Informations de copyright

© 2024. The Author(s).

Références

ICH Harmonised Tripartite Guideline E9. Statistical Principles for Clinical Trials. Sep 1998. https://www.fda.gov/media/71336/download
Rosenberger WF, Lachin J. Randomization in clinical trials: theory and practice. 2nd ed. New York: Wiley; 2015.
Hilgers RD, Uschner D, Rosenberger WF, Heussen N. ERDO – a framework to select an appropriate randomization procedure for clinical trials. BMC Med Res Methodol. 2017;17:159. https://doi.org/10.1186/s12874-017-0428-z .
doi: 10.1186/s12874-017-0428-z pubmed: 29202708 pmcid: 5715815
Berger VW, Bour LJ, Carter K, Chipman JJ, Everett CC, Heussen N, Hewitt C, Hilgers RD, Luo YA, Renteria J, Ryeznik Y, Sverdlov O, Uschner D, Randomization Innovative Design Scientific Working Group. A roadmap to using randomization in clinical trials. BMC Med Res Methodol. 2021;21(1):168. https://doi.org/10.1186/s12874-021-01303-z .
doi: 10.1186/s12874-021-01303-z pubmed: 34399696 pmcid: 8366748
Sverdlov O, Ryeznik Y, Anisimov V, Kuznetsova O, Knight R, Carter K, Drescher S, Zhao W. Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations. BMC Med Res Methodol. 2024;24:52. https://doi.org/10.1186/s12874-023-02131-z .
doi: 10.1186/s12874-023-02131-z pubmed: 38418968 pmcid: 10900599
Senn S. Some controversies in planning and analysing multi-centre trials. Stat Med. 1998;17(15–16):1753–65.
doi: 10.1002/(SICI)1097-0258(19980815/30)17:15/16<1753::AID-SIM977>3.0.CO;2-X pubmed: 9749445
Waters S, Dowlman I, Drake K, Gamble L, Lang M, McEntegart D. Enhancing control of the medication supply chain in clinical trials managed by interactive voice response systems. Drug Inform J. 2010;44:727–40. https://doi.org/10.1177/009286151004400609 .
doi: 10.1177/009286151004400609
Byrom B. Using IVRS in clinical trial management. Appl Clin Trials. 2002;10:36–42. https://www.appliedclinicaltrialsonline.com/view/using-ivrs-clinical-trial-management .
Kuznetsova OM. Letter to the editor – why permutation is even more important in IVRS drug codes schedule generation than in patient randomization schedule generation. Control Clin Trials. 2001;22:69–71. https://doi.org/10.1016/s0197-2456(00)00110-0 .
doi: 10.1016/s0197-2456(00)00110-0 pubmed: 11277091
McEntegart D. Forced randomization when using interactive voice response systems. Appl Clin Trials. 2003;12:50–8. https://www.appliedclinicaltrialsonline.com/view/forced-randomization-when-using-interactive-voice-response-systems .
Zelen M. The randomization and stratification of patients to clinical trials. J Chronic Disease. 1974;27:365–75. https://doi.org/10.1016/0021-9681(74)90015-0 .
doi: 10.1016/0021-9681(74)90015-0
McEntegart D. Block randomization. In: D’Agostino R, Sullivan L, Massaro J, editors Wiley Encyclopedia of clinical trials. Hoboken: John Wiley &Sons., Inc; June 13, 2008. https://doi.org/10.1002/9780471462422.eoct301
Morrissey M, McEntegart D, Lang M. Randomisation in double-blind multicentre trials with many treatments. Contemp Clin Trials. 2010;31:381–91. 10.1016./j/cct/2010.05.002.
doi: 10.1016/j.cct.2010.05.002 pubmed: 20460174
Krisam J, Ryeznik Y, Carter K, Kuznetsova O, Sverdlov O. Understanding an impact of patient enrollment pattern on predictability of central (unstratified) randomization in a multi-center clinical trial. Stat Med. 2024. https://doi.org/10.1002/sim.10117 . Epub ahead of print.
doi: 10.1002/sim.10117 pubmed: 38831520
Rosenkranz GK. The impact of randomization on the analysis of clinical trials. Stat Med. 2011;30:3475–87. https://doi.org/10.1002/sim.4376 .
doi: 10.1002/sim.4376 pubmed: 21953285
Uschner D, Sverdlov O, Carter K, Chipman J, Kuznetsova O, Renteria J, Lane A, Barker C, Geller N, Proschan M, Posch M, Tarima S, Bretz F, Rosenberger WF. Using randomization tests to address disruptions in clinical trials: a report from the NISS Ingram Olkin Forum series on unplanned clinical trial disruptions. Stat Biopharm Res. 2024. https://doi.org/10.1080/19466315.2023.2257894 .
doi: 10.1080/19466315.2023.2257894
Feuerstadt P, Louie TJ, Lashner B, Wang EEL, Diao L, Bryant JA, Sims M, Kraft CS, Cohen SH, Berenson CS, Korman LY, Ford CB, Litcofsky KD, Lombardo MJ, Wortman JR, Wu H, Aunins JG, McChalicher CWJ, Winkler JA, McGovern BH, Trucksis M, Henn MR, von Moltke L. SER-109, an oral microbiome therapy for recurrent Clostridioides difficile infection. N Engl J Med. 2022;386(3):220–9. https://doi.org/10.1056/NEJMoa2106516 .
doi: 10.1056/NEJMoa2106516 pubmed: 35045228
Lieberman JA, Davis RE, Correll CU, Goff DC, Kane JM, Tamminga CA, Mates S, Vanover KE. ITI-007 for the treatment of schizophrenia: a 4-week randomized, double-blind, controlled trial. Biol Psychiatry. 2016;79(12):952–61. https://doi.org/10.1016/j.biopsych.2015.08.026 .
doi: 10.1016/j.biopsych.2015.08.026 pubmed: 26444072
Rosenstock J, Kahn SE, Johansen OE, Zinman B, Espeland MA, Woerle HJ, Pfarr E, Keller A, Mattheus M, Baanstra D, Meinicke T, George JT, von Eynatten M, McGuire DK, Marx N, CAROLINA Investigators. Effect of linagliptin vs glimepiride on major adverse cardiovascular outcomes in patients with type 2 diabetes: the CAROLINA randomized clinical trial. JAMA. 2019;322(12):1155–66. https://doi.org/10.1001/jama.2019.13772 .
doi: 10.1001/jama.2019.13772 pubmed: 31536101
Bryant KA, Gurtman A, Girgenti D, Reisinger K, Johnson A, Pride MW, Patterson S, Devlin C, Gruber WC, Emini EA, Scott DA. Antibody responses to routine pediatric vaccines administered with 13-valent pneumococcal conjugate vaccine. Pediatr Infect Dis J. 2013;32(4):383–8. https://doi.org/10.1097/INF.0b013e318279e9a9 .
doi: 10.1097/INF.0b013e318279e9a9 pubmed: 23104129
Wilcox MH, Gerding DN, Poxton IR, Kelly C, Nathan R, Birch T, Cornely OA, Rahav G, Bouza E, Lee C, Jenkin G, Jensen W, Kim YS, Yoshida J, Gabryelski L, Pedley A, Eves K, Tipping R, Guris D, Kartsonis N, Dorr MB. MODIFY I and MODIFY II investigators. Bezlotoxumab for prevention of recurrent Clostridium difficile infection. N Engl J Med. 2017;376(4):305–17. https://doi.org/10.1056/NEJMoa1602615 .
doi: 10.1056/NEJMoa1602615 pubmed: 28121498
Colao A, Petersenn S, Newell-Price J, Findling JW, Gu F, Maldonado M, Schoenherr U, Mills D, Salgado LR, Biller BM. Pasireotide B2305 Study Group. A 12-month phase 3 study of pasireotide in Cushing’s disease. N Engl J Med. 2012;366(10):914–24. https://doi.org/10.1056/NEJMoa1105743 .
doi: 10.1056/NEJMoa1105743 pubmed: 22397653
Dematteo RP, Ballman KV, Antonescu CR, Maki RG, Pisters PW, Demetri GD, Blackstein ME, Blanke CD, von Mehren M, Brennan MF, Patel S, McCarter MD, Polikoff JA, Tan BR, Owzar K, American College of Surgeons Oncology Group (ACOSOG) Intergroup Adjuvant GIST Study Team. Adjuvant imatinib mesylate after resection of localised, primary gastrointestinal stromal tumour: a randomised, double-blind, placebo-controlled trial. Lancet. 2009;373(9669):1097–104. https://doi.org/10.1016/S0140-6736(09)60500-6 .
doi: 10.1016/S0140-6736(09)60500-6 pubmed: 19303137 pmcid: 2915459
Schulman S, Kearon C, Kakkar AK, Mismetti P, Schellong S, Eriksson H, Baanstra D, Schnee J, Goldhaber SZ, RE-COVER Study Group. Dabigatran versus warfarin in the treatment of acute venous thromboembolism. N Engl J Med. 2009;361(24):2342–52. https://doi.org/10.1056/NEJMoa0906598 .
doi: 10.1056/NEJMoa0906598 pubmed: 19966341
Schulman S, Kakkar AK, Goldhaber SZ, Schellong S, Eriksson H, Mismetti P, Christiansen AV, Friedman J, Le Maulf F, Peter N, Kearon C. RE-COVER II trial investigators. Treatment of acute venous thromboembolism with dabigatran or warfarin and pooled analysis. Circulation. 2014;129(7):764–72. https://doi.org/10.1161/CIRCULATIONAHA.113.004450 .
doi: 10.1161/CIRCULATIONAHA.113.004450 pubmed: 24344086
Deodhar A, Blanco R, Dokoupilová E, Hall S, Kameda H, Kivitz AJ, Poddubnyy D, van de Sande M, Wiksten AS, Porter BO, Richards HB, Haemmerle S, Braun J. Improvement of signs and symptoms of nonradiographic axial spondyloarthritis in patients treated with secukinumab: primary results of a randomized, placebo-controlled phase III study. Arthritis Rheumatol. 2021;73(1):110–20. https://doi.org/10.1002/art.41477 .
doi: 10.1002/art.41477 pubmed: 32770640
Hamilton SA. Dynamically allocating treatment when the cost of goods is high and drug supply is limited. Control Clin Trials. 2000;21:44–53. https://doi.org/10.1016/s0197-2456(99)00043-4 .
doi: 10.1016/s0197-2456(99)00043-4 pubmed: 10660003
Smith MK, Marshall A. Importance of protocols for simulation studies in clinical drug development. Stat Methods Med Res. 2011;20(6):613–22. https://doi.org/10.1177/0962280210378949 .
doi: 10.1177/0962280210378949 pubmed: 20688782
Mayer C, Perevozskaya I, Leonov S, Dragalin V, Pritchett Y, Bedding A, Harford A, Fardipour P, Cicconetti G. Simulation practices for adaptive trial designs in drug and device development. Stat Biopharm Res. 2019;11(4):325–35. https://doi.org/10.1080/19466315.2018.1560359 .
doi: 10.1080/19466315.2018.1560359
Anisimov VV, Fedorov VV. Modeling, prediction and adaptive adjustment of recruitment in multicentre trials. Stat Med. 2007;26(27):4958–75. https://doi.org/10.1002/sim.2956 .
doi: 10.1002/sim.2956 pubmed: 17639505
Anisimov VV, Fedorov VV. Design of multicentre clinical trials with random enrolment. In: Auget JL, Balakrishnan N, Mesbah M, Molenberghs G, editors. Advances in statistical methods for the Health sciences. Statistics for Industry and Technology. Birkhäuser Boston; 2006. pp. 387–400. https://doi.org/10.1007/978-0-8176-4542-7_25 .
doi: 10.1007/978-0-8176-4542-7_25
Anisimov VV. Drug supply modelling in clinical trials (statistical methodology). Pharmaceutical Outsourcing, May-June. 2010, Vol. 11, Issue 3, May/June 2010; pp. 17–20. https://www.pharmoutsourcing.com/Featured-Articles/37488-Drug-Supply-Modelling-in-Clinical-Trials-Statistical-Methodology/
Anisimov VV. Statistical modeling of clinical trials (recruitment and randomization). Commun Stat – Theory Methods. 2011;40(19–20):3684–99. https://doi.org/10.1080/03610926.2011.581189 .
doi: 10.1080/03610926.2011.581189
Patel NR, Ankolekar S, Senchaudhuri P. Approaches for clinical supply modelling and simulation. In He W, Pinheiro J, Kuznetsova OM, editors Practical considerations for adaptive Trial design and implementation. Statistics for Biology and Health, 2014, pp. 273–97. New York, NY: Springer. https://doi.org/10.1007/978-1-4939-1100-4
Lefew M, Ninh A, Anisimov V. End-to-end drug supply management in multicenter trials. Methodol Comput Appl Probab. 2021;23:695–709. https://doi.org/10.1007/s11009-020-09776-z .
doi: 10.1007/s11009-020-09776-z
Peterson M, Byrom B, Dowlman N, McEntegart D. Optimizing clinical trial supply requirements: simulation of computer-controlled supply chain management. Clin Trails. 2004;1(4):399–412. https://doi.org/10.1191/1740774504cn037oa .
doi: 10.1191/1740774504cn037oa
Kuznetsova OM, Tymofyeyev Y. Brick tunnel randomization for unequal allocation to two or more treatment groups. Stat Med. 2011;30:812–24. https://doi.org/10.1002/sim.4167 .
doi: 10.1002/sim.4167 pubmed: 21432876
Zhao W, Weng Y. Block urn design—A new randomization algorithm for sequential trials with two or more treatments and balanced or unbalanced allocation. Contemp Clin Trials. 2011;32:953–61. https://doi.org/10.1016/j.cct.2011.08.004 .
doi: 10.1016/j.cct.2011.08.004 pubmed: 21893215 pmcid: 3206733
Zhao W. Mass weighted urn design—a new randomization algorithm for unequal allocations. Contemp Clin Trials. 2015;43:209–16. https://doi.org/10.1016/j.cct.2015.06.008 .
doi: 10.1016/j.cct.2015.06.008 pubmed: 26091947 pmcid: 4522356
Sverdlov O, Glimm E, Mesenbrink P. Platform trial designs. In S. Piantadosi and C. Meinert, editors, Principles and Practice of Clinical Trials. 2021. Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-319-52677-5_107-1
Sadek NL, Costa BA, Nath K, Mailankody S. CAR T-Cell therapy for multiple myeloma: a clinical practice-oriented review. Clin Pharmacol Ther. 2023;114(6):1184–95. https://doi.org/10.1002/cpt.3057 .
doi: 10.1002/cpt.3057 pubmed: 37750399

Auteurs

Kerstine Carter (K)

Boehringer-Ingelheim Pharmaceuticals Inc, Ridgefield, CT, USA.

Olga Kuznetsova (O)

Merck & Co., Inc., Rahway, NJ, USA.

Volodymyr Anisimov (V)

Amgen Ltd., London, UK.

Johannes Krisam (J)

Boehringer-Ingelheim Pharma GmbH & Co. KG, Ingelheim, Germany.

Colin Scherer (C)

Rensselaer Polytechnic Institute, Troy, NY, USA.

Yevgen Ryeznik (Y)

Department of Pharmacy, Uppsala University, Uppsala, Sweden.

Oleksandr Sverdlov (O)

Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA. alex.sverdlov@novartis.com.

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