A roadmap to using randomization in clinical trials

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.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.

Novel clinical trial design and analytic methods to tackle challenges in therapeutic development in rare diseases

“…For randomized clinical trials (RCTs), the most common approach to inference on the treatment effect is the likelihood-based inference, such as a two-sample t-test for normal outcome data and chi-square test for binary Ann Transl Med 2022;10(18):1034 | https://dx.doi.org/10.21037/atm-21-5496 outcome data. Randomization-based inference (RBI) is an alternative approach, where the implanted randomization forms the basis for statistical inference (103). In this approach, treatment assignments are permuted in all possible ways that are consistent with the randomization procedure used in the trial, and a P value is calculated based on these permutations.…”