Topoi

pp 1–7 | Cite as

The Role of Randomization in Bayesian and Frequentist Design of Clinical Trial

Article
  • 10 Downloads

Abstract

A key role in inference is played by randomization, which has been extensively used in clinical trials designs. Randomization is primarily intended to prevent the source of bias in treatment allocation by producing comparable groups. In the frequentist framework of inference, randomization allows also for the use of probability theory to express the likelihood of chance as a source for the difference of end outcome. In the Bayesian framework, its role is more nuanced. The Bayesian analysis of clinical trials can afford a valid rationale for selective controls, pointing out a more limited role for randomization than it is generally accorded. This paper is aimed to offer a view of randomization from the perspective of both frequentist and Bayesian statistics and discussing the role of randomization also in theoretical decision models.

Keywords

Clinical trials Bayesian inference Frequentist inference Randomization 

Notes

Acknowledgements

This research was supported by the University of Torino, Grant No. BERP_RILO_17_01.

References

  1. Altman DG, Schulz KF, Moher D, Egger M, Davidoff F, Elbourne D, Gøtzsche PC, Lang T, the CONSORT Group (2001) The revised CONSORT statement for reporting randomised trials: explanation and elaboration. Ann Intern Med 134:663–694Google Scholar
  2. Bartlett RH, Roloff DW, Cornell RG, Andrews AF, Dillon PW, Zwischenberger JB (1985) Extracorporeal circulation in neonatal respiratory failure: a prospective randomized study. Pediatrics 76(4):479–487Google Scholar
  3. Berger VW (2000) Pros and cons of permutation tests in clinical trials. Stat Med 19(10):1319–1328CrossRefGoogle Scholar
  4. Berry SM, Kadane JB (1997) Optimal Bayesian randomization. J R Stat Soc 59(4):813–819CrossRefGoogle Scholar
  5. Berry SM, Bradley P, Carlin J, Lee JJ, Muller P (2010) Bayesian adaptive methods for clinical trials, Chapman & Hall, Boca RatonCrossRefGoogle Scholar
  6. Biswas S, Liu DD, Lee JJ, Berry DA (2009) Bayesian clinical trials at the University of Texas M. D. Anderson Cancer Center. Clin Trials 6(3):205–216CrossRefGoogle Scholar
  7. Brown AR, Gajewski BJ, Aaronson LS et al (2016) A Bayesian comparative effectiveness trial in action: developing a platform for multisite study adaptive randomization. Trials 17(1):428CrossRefGoogle Scholar
  8. Christen JA, Muller P, Wathen K, Wolf J (2004) Bayesian randomized clinical trials: a decision-theoretic sequential design. Can J Stat 32(4):387–402CrossRefGoogle Scholar
  9. Cox DR (2009) Randomization in the design of experiments. Int Stat Rev 77:415–429CrossRefGoogle Scholar
  10. Cumberland WG, Royall RM (1988) Does simple random sampling provide adequate balance? J R Stat Soc 50:118–124Google Scholar
  11. Efron B (1971) Forcing a sequential experiment to be balanced. Biometrika 58(3):403–417CrossRefGoogle Scholar
  12. Fisher RA (1935) The design of experiments. Oliver and Boyd, EdinburghGoogle Scholar
  13. Gelman A (2008) Rejoinder. Bayesian Anal 3(3):467–478CrossRefGoogle Scholar
  14. Gelman A, Carlin J, Stern H, Dunson D, Vehtari A, Rubin D (2014) Bayesian data analysis, 3rd Edition, Chapman and Hall/CRC, Boca RatonGoogle Scholar
  15. Hacking I (1988) Telepathy: origins of randomization in experimental design. Isis 79:427–451CrossRefGoogle Scholar
  16. Hall NS (2007) RA Fisher and his advocacy of randomization. J Hist Biol 40(2):295–325CrossRefGoogle Scholar
  17. Hey SP, Truog RD (2015) The question of clinical equipoise and patients’ best interests. AMA J Ethics 17(12):1108–1115CrossRefGoogle Scholar
  18. Holland PW (1986) Statistics and causal inference. J Am Stat Assoc 81(396):945–960CrossRefGoogle Scholar
  19. Jiang F, Lee JJ, Muller P (2013) A Bayesian decision-theoretic sequential response-adaptive randomization design. Stat Med 32(12):1975–1994CrossRefGoogle Scholar
  20. Lachin JM (1988) Statistical properties of randomization in clinical trials. Control Clin Trials 9(4):289–311CrossRefGoogle Scholar
  21. Lee JJ, Chu CT (2012) Bayesian clinical trials in action. Stat Med 31(25):2955–2972CrossRefGoogle Scholar
  22. Lee JJ, Chen N, Yin G (2012) Worth adapting? Revisiting the usefulness of outcome-adaptive randomization. Clin Cancer Res 18(17):4498–4507CrossRefGoogle Scholar
  23. Lin Y, Zhu M, Su Z (2015) The pursuit of balance: an overview of covariate-adaptive randomization techniques in clinical trials. Contemp Clin Trials 45(Pt A):21–25CrossRefGoogle Scholar
  24. Little RJ (2006) Calibrated Bayes. Am Stat 60(3):213–223CrossRefGoogle Scholar
  25. Medical Research Council (1948) STREPTOMYCIN treatment of pulmonary tuberculosis. Br Med J 2(4582): 769–782CrossRefGoogle Scholar
  26. Rosenberger WF (2010) The agile approach to adaptive research. Wiley, New JerseyCrossRefGoogle Scholar
  27. Rosenberger WF, Lachin JM (1993) The use of response-adaptive designs in clinical trials. Control Clin Trials 14(6):471–484CrossRefGoogle Scholar
  28. Rosenberger WF, Lachin JM (2016) Randomization in clinical trials: theory and practice. Wiley, New YorkCrossRefGoogle Scholar
  29. Rosenberger WF, Stallard N, Ivanova A, Harper CN, Ricks ML (2001) Optimal adaptive designs for binary response trials. Biometrics 57(3):909–913CrossRefGoogle Scholar
  30. Rosner AL (2012) Evidence-based medicine: revisiting the pyramid of priorities. J Bodyw Mov Ther 16(1):42–49CrossRefGoogle Scholar
  31. Rubin DB (1978) Bayesian inference for causal effects: the role of randomization. Ann Stat 6(1):34–58CrossRefGoogle Scholar
  32. Rubin DB (1991) Practical implications of modes of statistical inference for causal effects and the critical role of the assignment mechanism. Biometrics 47(4):1213–1234CrossRefGoogle Scholar
  33. Sackett DL, Rosenberg WM, Gray JA, Haynes RB, Richardson WS (1996) Evidence based medicine: what it is and what it isn’t. BMJ 312(7023):71–72CrossRefGoogle Scholar
  34. Savage LJ (1962) Subjective probability and statistical practice. In: Savage LJ et al (ed) The foundations of statistical inference. Methuen, LondonGoogle Scholar
  35. Saxman SB (2015) Ethical considerations for outcome-adaptive trial designs: a clinical researcher’s perspective. Bioethics 29(2):59–65CrossRefGoogle Scholar
  36. Spiegelhalter D, Abrams K, Myles J (2004) Bayesian approaches to clinical trials and health care evaluation. Wiley, ChichesterGoogle Scholar
  37. Thall PF, Wathen JK (2007) Practical Bayesian adaptive randomisation in clinical trials. Eur J Cancer 43(5):859–866CrossRefGoogle Scholar
  38. Urbach P (1993) The value of randomization and control in clinical trials. Stat Med 12(15–16):1421–1431. (discussion 1433–1441)CrossRefGoogle Scholar
  39. Wei IJ, Durham S (1978) The randomized play-the-winner rule in medical trials. J Am Stat Assoc 73:840–843CrossRefGoogle Scholar
  40. Worrall J (2007) Why there’s no cause to randomize. Br J Philos Sci 58(3):451–488CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Clinical and Biological SciencesUniversity of TorinoTurinItaly
  2. 2.Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular SciencesUniversity of PadovaPaduaItaly

Personalised recommendations