Literature DB >> 26337607

Comparing MTI randomization procedures to blocked randomization.

Vance W Berger1,2, Klejda Bejleri3, Rebecca Agnor4.   

Abstract

Randomization is one of the cornerstones of the randomized clinical trial, and there is no shortage of methods one can use to randomize patients to treatment groups. When deciding which one to use, researchers must bear in mind that not all randomization procedures are equally adept at achieving the objective of randomization, namely, balanced treatment groups. One threat is chronological bias, and permuted blocks randomization does such a good job at controlling chronological bias that it has become the standard randomization procedure in clinical trials. But permuted blocks randomization is especially vulnerable to selection bias, so as a result, the maximum tolerated imbalance (MTI) procedures were proposed as better alternatives. In comparing the procedures, we have somewhat of a false controversy, in that actual practice goes uniformly one way (permuted blocks), whereas scientific arguments go uniformly the other way (MTI procedures). There is no argument in the literature to suggest that the permuted block design is better than or even as good as the MTI procedures, but this dearth is matched by an equivalent one regarding actual trials using the MTI procedures. So the 'controversy', if we are to call it that, pits misguided precedent against sound advice that tends to be ignored in practice. We shall review the issues to determine scientifically which of the procedures is better and, therefore, should be used.
Copyright © 2015 John Wiley & Sons, Ltd.

Entities:  

Keywords:  MTI procedures; inertia; maximal procedure; permuted blocks; randomization

Mesh:

Year:  2015        PMID: 26337607     DOI: 10.1002/sim.6637

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  8 in total

1.  Implementing Optimal Designs for Dose-Response Studies Through Adaptive Randomization for a Small Population Group.

Authors:  Yevgen Ryeznik; Oleksandr Sverdlov; Andrew C Hooker
Journal:  AAPS J       Date:  2018-07-19       Impact factor: 4.009

2.  Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint.

Authors:  Steven D Lauzon; Wenle Zhao; Paul J Nietert; Jody D Ciolino; Michael D Hill; Viswanathan Ramakrishnan
Journal:  Stat Methods Med Res       Date:  2021-11-29       Impact factor: 2.494

3.  Risk of selection bias in randomized trials: further insight.

Authors:  Vance W Berger
Journal:  Trials       Date:  2016-10-07       Impact factor: 2.279

4.  ERDO - a framework to select an appropriate randomization procedure for clinical trials.

Authors:  Ralf-Dieter Hilgers; Diane Uschner; William F Rosenberger; Nicole Heussen
Journal:  BMC Med Res Methodol       Date:  2017-12-04       Impact factor: 4.615

5.  Assessing the impact of selection bias on test decisions in trials with a time-to-event outcome.

Authors:  Marcia Viviane Rückbeil; Ralf-Dieter Hilgers; Nicole Heussen
Journal:  Stat Med       Date:  2017-04-17       Impact factor: 2.373

6.  Ideal vs. real: a systematic review on handling covariates in randomized controlled trials.

Authors:  Jody D Ciolino; Hannah L Palac; Amy Yang; Mireya Vaca; Hayley M Belli
Journal:  BMC Med Res Methodol       Date:  2019-07-03       Impact factor: 4.615

7.  Simulation and minimization: technical advances for factorial experiments designed to optimize clinical interventions.

Authors:  Jocelyn Kuhn; Radley Christopher Sheldrick; Sarabeth Broder-Fingert; Andrea Chu; Lisa Fortuna; Megan Jordan; Dana Rubin; Emily Feinberg
Journal:  BMC Med Res Methodol       Date:  2019-12-16       Impact factor: 4.615

8.  The impact of selection bias in randomized multi-arm parallel group clinical trials.

Authors:  Diane Uschner; Ralf-Dieter Hilgers; Nicole Heussen
Journal:  PLoS One       Date:  2018-01-31       Impact factor: 3.240

  8 in total

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