The Role of Randomization in Bayesian and Frequentist Design of Clinical Trial
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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.
KeywordsClinical trials Bayesian inference Frequentist inference Randomization
This research was supported by the University of Torino, Grant No. BERP_RILO_17_01.
- 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
- 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
- Cumberland WG, Royall RM (1988) Does simple random sampling provide adequate balance? J R Stat Soc 50:118–124Google Scholar
- Fisher RA (1935) The design of experiments. Oliver and Boyd, EdinburghGoogle Scholar
- 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
- Savage LJ (1962) Subjective probability and statistical practice. In: Savage LJ et al (ed) The foundations of statistical inference. Methuen, LondonGoogle Scholar
- Spiegelhalter D, Abrams K, Myles J (2004) Bayesian approaches to clinical trials and health care evaluation. Wiley, ChichesterGoogle Scholar