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Randomization Challenges in Adaptive Design Studies

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Book cover Practical Considerations for Adaptive Trial Design and Implementation

Part of the book series: Statistics for Biology and Health ((SBH))

Abstract

Adaptive design studies often face randomization challenges. Adaptive dose-ranging studies require randomization techniques that, in a small cohort, approximate reasonably well an inconveniently skewed allocation ratio to several treatment arms. When a small interim analysis sample needs to be balanced in several important predictors, dynamic allocation might be required to achieve this goal. Accelerated drug development often necessitates a large number of centers to speed up the study enrollment. When the drug is limited or costly, as is often the case with adaptive design studies conducted early in drug development, advanced randomization techniques are needed to efficiently manage the drug supplies in multicenter trials. In open-label adaptive design trials randomization procedures less predictable than permuted block randomization help reduce potential for selection bias. Randomization techniques developed for equal allocation to several treatment arms help dealing with the randomization challenges in equal allocation adaptive design studies. When these techniques are expanded to unequal allocation common to adaptive designs, care should be taken to preserve the allocation ratio at every allocation step. In this chapter we review randomization techniques useful in adaptive design studies, including those developed in recent years to specifically address the needs above.

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References

  • Abel U (1987) Modified replacement randomization. Stat Med 6:127–135

    Article  Google Scholar 

  • Akazawa K, Odaka T, Sakamoto M, Ohtsuki S, Shimada M, Kamakura T, Nose Y (1991) A random allocation system with the minimization method for multi-institutional clinical trials. J Med Syst 15(4):311–319

    Article  Google Scholar 

  • Antognini AB, Giovagnoli A (2004) A new ‘biased coin design’ for the sequential allocation of two treatments. J Roy Stat Soc C 53:651–664

    Article  MATH  Google Scholar 

  • Begg CB, Iglewicz B (1980) A treatment allocation procedure for sequential clinical trials. Biometrics 36:81–90

    Article  MATH  Google Scholar 

  • Berger VW, Ivanova A, Knoll M (2003) Minimizing predictability while retaining balance through the use of less restrictive randomization procedures. Stat Med 22:3017–3028. doi:10.1002/sim.1538

    Article  Google Scholar 

  • Birkett NJ (1985) Adaptive allocation in randomized controlled trials. Control Clin Trials 6:146–155

    Article  Google Scholar 

  • Buyse M, McEntegart D (2004) Achieving balance in clinical trials: an unbalanced view from EU regulators. Appl Clin Trials 13:36–40

    Google Scholar 

  • Byrom B, McEntegart D, Nicholls G (2011) Adaptive infrastructure. In: Pong A, Chow S-C (eds) Handbook of adaptive designs in pharmaceutical and clinical development. Taylor and Francis Group, London, pp 20-1–20-25

    Google Scholar 

  • Chen YP (1999) Biased coin design with imbalance tolerance. Comm Stat Stoch Model 15:953–975

    Article  MATH  Google Scholar 

  • Chen YP (2000) Which design is better? Ehrenfest urn versus biased coin. Adv Appl Probab 32:738–749

    Article  MATH  Google Scholar 

  • Committee for Proprietary Medicinal Products (CPMP) (2003) Points to consider on adjustment for baseline covariates. European Medicines Agency, London

    Google Scholar 

  • Downs M, Tucker K, Christ-Schmidt H, Wittes J (2010) Some practical problems in implementing randomization. Clin Trials 7:235–245

    Article  Google Scholar 

  • Efron B (1971) Forcing a sequential experiment to be balanced. Biometrika 58:403–417

    Article  MathSciNet  MATH  Google Scholar 

  • EMA Committee for Medicinal Products for Human Use. Guideline on adjustment for baseline covariates. Draft. 26 Apr 2013

    Google Scholar 

  • Forsythe AB (1987) Validity and power of tests when groups have been balanced for prognostic factors. Comput Stat Data Anal 5:193–200

    Article  Google Scholar 

  • Frane JW (1998) A method of biased coin randomization, its implementation, and its validation. Drug Inf J 32:423–432, 0092-8615/98

    Google Scholar 

  • Gaydos B, Krams M, Perevozskaya I, Bretz F, Liu Q, Gallo P (2006) PhRMA working group on adaptive designs: adaptive dose–response studies. Drug Inf J 40:451–461

    Google Scholar 

  • Goodale H, McEntegart D (2013) The role of technology in avoiding bias in the design and execution of clinical trials. Open Access J Clin Trials 5:13–21

    Article  Google Scholar 

  • Han B, Enas NH, McEntegart D (2009) Randomization by minimization for unbalanced treatment allocation. Stat Med 28:3329–3346. doi:10.1002/sim.3710

    Article  MathSciNet  Google Scholar 

  • Han B, Yu M, McEntegart D (2013) Weighted re-randomization tests for minimization with unbalanced allocation. Pharm Stat 12:243–253. doi:10.1002/pst.1577

    Article  Google Scholar 

  • He W, Kuznetsova OM, Harmer MA, Leahy CJ, Anderson KM, Dossin DN, Li L, Bolognese JA, Tymofyeyev Y, Schindler JS (2012) Practical considerations and strategies for executing adaptive clinical trials. Drug Inf J 46:160–174. doi:10.1177/0092861512436580

    Google Scholar 

  • Heritier S, Gebski V, Pillai A (2005) Dynamic balancing randomization in controlled clinical trials. Stat Med 24:3729–3741. doi:10.1002/sim.2421

    Article  MathSciNet  Google Scholar 

  • Hu F., Hu Y, Ma Z, Rosenberger WF (2014) Adaptive randomization for balancing over covariates. Wiley Interdisciplinary Reviews: Computational Statistics, 6, 288–303

    Google Scholar 

  • ICH (1998) ICH Topic E9: statistical principles for clinical trials, available at http://www.ich.org/LOB/media/MEDIA485.pdf

  • Kaiser LD (2012) Dynamic randomization and a randomization model for clinical trials data. Stat Med 31:3858–3873. doi:10.1002/sim.5448

    Article  MathSciNet  Google Scholar 

  • Kalish LA, Begg CB (1985) Treatment allocation methods in clinical trials: a review. Stat Med 4:129–144

    Article  Google Scholar 

  • Kalish LA, Begg CB (1987) The impact of treatment allocation procedures on nominal significance levels and bias. Control Clin Trials 8:121–135

    Article  Google Scholar 

  • Kuznetsova OM (2001) Why permutation is even more important in IVRS drug codes schedule generation than in patient randomization schedule generation. Control Clin Trials 22:69–71, Letter to the Editor

    Article  Google Scholar 

  • Kuznetsova OM (2008) Randomization schedule. In: D’Agostino R, Sullivan L, Massaro J (eds) Wiley encyclopedia of clinical trials. Wiley, Hoboken, NJ. doi:10.1002/9780471462422.eoct314, Published Online: 19 Sep 2008

    Google Scholar 

  • Kuznetsova OM (2010) On the second role of the random element in minimization. Short communication regarding the short communication by D. Taves on “The Use of Minimization in Clinical Trials”. Contemp Clin Trials 31:587–588. doi:10.1016/j.cct.2010.07.010

    Article  Google Scholar 

  • Kuznetsova OM (2012) Considerations in the paper by Proschan, Brittain, and Kammerman are not an argument against minimization. In response to Vance W Berger ‘Minimization: not all it’s cracked up to be’, Clin Trials 2011; 8: 443. Clin Trials 9:370

    Article  Google Scholar 

  • Kuznetsova OM, Ivanova A (2006) Allocation in randomized clinical trials. In: Dmitrienko A, Chuang-Stein C, D’Agostino R (eds) Pharmaceutical statistics using SAS. SAS Press, Cary, NC, pp 213–236

    Google Scholar 

  • Kuznetsova OM, Tymofyeyev Y (2009) Brick tunnel randomization—a way to accommodate a problematic allocation ratio in adaptive design dose finding studies. ASA proceedings of the joint statistical meetings. American Statistical Association, Alexandria, VA, pp 1356–1367

    Google Scholar 

  • Kuznetsova OM, Tymofyeyev Y (2011a) Brick tunnel randomization for unequal allocation to two or more treatment groups. Stat Med 30:812–824. doi:10.1002/sim.4167

  • Kuznetsova OM, Tymofyeyev Y (2011b) Expansion of the modified Zelen’s approach randomization and dynamic randomization with partial block supplies at the centers to unequal allocation. ASA proceedings of the joint statistical meetings. American Statistical Association, Miami Beach, FL

    Google Scholar 

  • Kuznetsova OM, Tymofyeyev Y (2011c) Expansion of the modified Zelen’s approach randomization and dynamic randomization with partial block supplies at the centers to unequal allocation. Contemp Clin Trials 32:962–972. doi:10.1016/j.cct.2011.08.006

    Article  Google Scholar 

  • Kuznetsova OM, Tymofyeyev Y (2012) Preserving the allocation ratio at every allocation with biased coin randomization and minimization in studies with unequal allocation. Stat Med 31:701–723. doi:10.1002/sim.4447

    Article  MathSciNet  Google Scholar 

  • Kuznetsova OM, Tymofyeyev Y (2013a) Shift in re-randomization distribution with conditional randomization test. Pharmaceut Stat 12:82–91. doi:10.1002/pst.1556

    Article  Google Scholar 

  • Kuznetsova OM, Tymofyeyev Y (2013b) Expanding brick tunnel randomization to allow for larger imbalance in treatment totals in studies with unequal allocation. Proceedings of the joint statistical association 2013 meetings, Montreal, QC, Canada, 4–8 Aug 2013

    Google Scholar 

  • Kuznetsova OM, Tymofyeyev Y (2014a) Wide Brick tunnel randomization—an unequal allocation procedure that limits the imbalance in treatment totals. Stat Med 33:1514–1530. doi:10.1002/sim.6051

    Article  MathSciNet  Google Scholar 

  • Kuznetsova OM, Tymofyeyev Y (2014b) Hierarchical dynamic allocation procedures based on modified Zelen’s approach in multi-regional studies with unequal allocation. J Biopharm Stat 24:1–17

    Article  MathSciNet  Google Scholar 

  • Lang M, Wood R, McEntegart D (2005) Protecting the blind. GCPj p. 10 Nov 2005 14/11/05 3:39 pm

    Google Scholar 

  • Ma W, Hu F (2013) Hypothesis testing of covariate-adaptive randomized clinical trials under generalized linear models. Paper presented at Joint Statistical Association 2013 Meetings, Montreal, Canada, 4–8 Aug 2013

    Google Scholar 

  • Markaryan T, Rosenberger WF (2010) Exact properties of Efron’s biased coin randomization procedure. Ann Stat 38:1546–1567. doi:10.1214/09-AOS758

    Article  MathSciNet  MATH  Google Scholar 

  • McEntegart D (2002) Forced randomization when using interactive voice response systems. Appl Clin Trials 12(10):2–10

    Google Scholar 

  • McEntegart D (2003) The pursuit of balance using stratified and dynamic randomization techniques: an overview. Drug Inf J 37:293–308

    Article  Google Scholar 

  • McEntegart D (2008) Blocked randomization. In: D’Agostino R, Sullivan L, Massaro J (eds) Wiley encyclopedia of clinical trials. Wiley, Hoboken. DOI:10.1002/9780471462422.eoct301. Accessed 13 June 2008

    Google Scholar 

  • Morrissey M, McEntegart D, Lang M (2010) Randomisation in double-blind multicentre trials with many treatments. Contemp Clin Trials 31:381–391. doi:10.1016./j/cct/2010.05.002

    Article  Google Scholar 

  • Nishi T, Takaishi A (2003) An extended minimization method to assure similar means of continuous prognostic variable between treatment groups. Jpn J Biomet 24:43–55

    Article  Google Scholar 

  • Parke T (2008) Adaptive clinical trials in the real world. Paper presented at Massachusetts Biotechnology Council, 23 Apr 2008, Cambridge, MA

    Google Scholar 

  • Pocock SJ (1979) Allocation of patients to treatment in clinical trials. Biometrics 35:183–197

    Article  Google Scholar 

  • Pocock SJ, Simon R (1975) Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. Biometrics 31:103–115

    Article  Google Scholar 

  • Pond GR, Tang PA, Welch SA, Chen EX (2010) Trends in the application of dynamic allocation methods in multi-arm cancer clinical trials. Clin Trials 7(3):227–234

    Article  Google Scholar 

  • Proschan M, Brittain E, Kammerman L (2011) Minimize the use of minimization with unequal allocation. Biometrics 67(3):1135–1141. doi:10.1111/j.1541-0420.2010.01545.x

    Article  MathSciNet  MATH  Google Scholar 

  • Rosenberger WF, Lachin JM (2002) Randomization in clinical trials. Wiley, New York, NY

    Book  MATH  Google Scholar 

  • Rosenberger WF, Sverdlov O (2008) Handling covariates in the design of clinical trials. Stat Sci 23:404–419

    Article  MathSciNet  Google Scholar 

  • Salama I, Ivanova A, Qaqish B (2008) Efficient generation of constrained block allocation sequences. Stat Med 27:1421–1428. doi:10.1002/sim3014

    Article  MathSciNet  Google Scholar 

  • Scott NW, McPherson GC, Ramsay CR, Campbell MK (2002) The method of minimization for allocation to clinical trials: a review. Control Clin Trials 23:662–674

    Article  Google Scholar 

  • Shao J, Yu X (2013) Validity of tests under covariate-adaptive biased coin randomization and generalized linear models. Biometrics 69:960–969. doi:10.1111/biom.12062

    Article  MathSciNet  MATH  Google Scholar 

  • Shao J, Yu X, Zhong B (2010) A theory of testing hypotheses under covariate adaptive randomization. Biometrika 97:347–360

    Article  MathSciNet  MATH  Google Scholar 

  • Signorini DF, Leung O, Simes RJ, Beller E, Gebski VJ (1993) Dynamic balanced randomisation for clinical trials. Stat Med 12:2343–2350

    Article  Google Scholar 

  • Soares JF, Wu CF (1983) Some restricted randomization rules in sequential designs. Comm Stat Theor Meth 12:2017–2034

    Article  MathSciNet  MATH  Google Scholar 

  • Song C, Kuznetsova OM (2003) Implementing Constrained or Balanced Across the Centers Randomization with SAS v8 Procedure PLAN, PharmaSUG 2003 proceedings, Miami, FL, pp. 473–479. Accessed 4–7 May 2003

    Google Scholar 

  • Taves D (1974) Minimization: a new method of assigning subjects to treatment and control groups. Clin Pharmacol Ther 15:443–453

    Google Scholar 

  • Therneau TM (1993) How many stratification factors are “too many” to use in a randomization plan? Control Clin Trials 14(2):98–108

    Article  Google Scholar 

  • Weir CJ, Lees KR (2003) Comparison of stratification and adaptive methods for treatment allocation in an acute stroke clinical trial. Stat Med 22:705–726

    Article  Google Scholar 

  • Youden WJ (1964) Inadmissible random assignments. Technometrics 6:103–104

    Google Scholar 

  • Youden WJ (1972) Randomization and experimentation. Technometrics 14:13–22

    Article  Google Scholar 

  • Zelen M (1974) The randomization and stratification of patients to clinical trials. J Chronic Dis 27:365–375

    Article  MathSciNet  Google Scholar 

  • Zhao W, Weng Y (2011) Block urn design—a new randomization algorithm for sequential trials with two or more treatments and balanced or unbalanced allocation. Contemp Clin Trials 32(6):953–961

    Article  Google Scholar 

  • Zielhuis GA, Straatman H, van’T Hof-Grootenboer AE, van Lier HJJ, Rach GH, van den Broek P (1990) The choice of a balanced allocation method for a clinical trial in otitis media with effusion. Stat Med 9:237–246

    Article  Google Scholar 

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Authors and Affiliations

  1. Clinical Biostatistics, Merck Research Laboratories, RY34-A316, 126 Lincoln Avenue, Rahway, NJ, 07065, USA

    Olga M. Kuznetsova

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  1. Olga M. Kuznetsova
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Correspondence to Olga M. Kuznetsova .

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Editors and Affiliations

  1. Department of Clinical Biostatistics, Merck & Co., Inc., Rahway, New Jersey, USA

    Weili He

  2. Johnson & Johnson, Raritan, New Jersey, USA

    José Pinheiro

  3. Department of Clinical Biostatistics, Merck & Co., Inc., Rahway, New Jersey, USA

    Olga M. Kuznetsova

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Kuznetsova, O.M. (2014). Randomization Challenges in Adaptive Design Studies. In: He, W., Pinheiro, J., Kuznetsova, O. (eds) Practical Considerations for Adaptive Trial Design and Implementation. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1100-4_9

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