Properties of the Random Block Design for Clinical Trials

Shao, Hui; Rosenberger, William F.

doi:10.1007/978-3-319-31266-8_26

Cited by 4 publications

(1 citation statement)

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“…Larger block sizes increase allocation randomness. The use of variable block sizes has been suggested [31]; however, PBDs with variable block sizes are also quite predictable [32]. Another problematic feature of the PBD is that it forces periodic return to perfect balance, which may be unnecessary from the statistical efficiency perspective and may increase the risk of prediction of upcoming allocations.…”

Section: What Types Of Randomization Methodologies Are Available?mentioning

confidence: 99%

A roadmap to using randomization in clinical trials

Berger

Bour

Carter

et al. 2021

Preprint

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Background: Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available.Methods: We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice. Results: Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size.Conclusions: The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.

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Section: What Types Of Randomization Methodologies Are Available?mentioning

confidence: 99%