AI Article Synopsis
- Randomization is crucial in clinical trials as it reduces selection bias, ensures balanced treatment groups, and enhances the validity of statistical tests; the paper discusses various restricted randomization methods for improving these processes.
- The authors evaluate multiple restricted randomization procedures, analyzing their statistical properties such as balance and error rates, and comparing their effectiveness through simulations and real-life examples.
- The findings highlight that different procedures have varying strengths, especially when assumptions are violated, and emphasize the importance of covariate-adjusted analysis and careful selection of randomization methods, especially with small sample sizes.
Article Abstract
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. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial.
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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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8366748 | PMC |
| http://dx.doi.org/10.1186/s12874-021-01303-z | DOI Listing |
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