Learning
Regulatory Guidance on Randomization and the Use of Randomization Tests in Clinical Trials
Randomization is a critical aspect of the clinical trial. Regulatory guidance plays a role in clinical research. Randomization tests provide valid methodology for statistical inference. In the present paper, we intended to survey: (a) information contained in regulatory guidance on randomization; and (b) the use of randomization tests in trials supporting marketing applications. This systematic review used the Cortellis Regulatory Intelligence database (IDRAC). For (a), of n = 156 guidance documents, nine provided recommendations on randomization methods. For (b), n = 48 trials (52 submissions) employed randomization tests. 40 (83.3%) were phase III trials, 31 (64.6%) employed dynamic allocation. Randomization test was the primary in 5 (10.4%), and sensitivity analysis in 34 (70.8%) trials...
Better alternatives to permuted block randomization for clinical trials with unequal allocation
Meurer, Connor, and Glassberg published ‘Simulation of various randomization strategies for a clinical trial in sickle cell disease’, proposing a biased-coin adaptive randomization design for a trial with a small sample size of 45 and an unequal target allocation of 1:2 between placebo and active treatment, and compared it with the complete randomization and the permuted block randomization via computer simulation. The authors’ effort of seeking a better alternative to the inferior permuted block randomization is laudable, and the implementation of better randomization designs in trials is important. Full Article: https://www.tandfonline.com/doi/full/10.1080/10245332.2016.1236996
A clinical trial randomization tool offered by the National Cancer Institute
https://ctrandomization.cancer.gov/
R code to perform several restricted randomization procedures targeting unequal allocation in multi-arm clinical trial
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Implementing Optimal Designs for Dose-Response Studies through Adaptive Randomization for a Small Population Group.
In dose-response studies with censored time-to-event outcomes, D-optimal designs depend on the true model parameters and the number of censored outcomes. In order to implement such a design in practice, an adaptive design that incorporates updated knowledge about the dose-response curve at interim analyses can be used [1]. Further, treatment allocation should involve randomization, which is essential to mitigate various experimental biases and perform valid statistical inference at the end of the trial. Here, we compare several randomization procedures and their impact on model estimation. Full Article: Implementing Optimal Designs
