Module 4-2 – Unit of Randomization & Common Allocation Techniques (20-Minute Video)

• Randomization is at the heart of clinical trials — but what does it really mean to randomize individuals vs. clusters? In this module, we learn why some interventions are randomized at the patient level while others randomize groups of participants within entire schools, clinics, or hospitals. We’ll also walk through common allocation techniques — simple, blocked, stratified, minimization, matching, and covariate-constrained randomization — with practical examples that make the concepts click.

Presentation Slides

** The video's content and narration were generated with the assistance of artificial intelligence, with human guidance and oversight throughout the process. **

 Adaptive randomization for balancing over covariates (Source) 

Abstract: In controlled clinical trials, balanced allocation over covariates is often viewed as an essential component in ensuring valid treatment comparisons. Minimization, sometimes called ‘dynamic allocation’, or ‘covariate-adaptive randomization’ has an advantage over stratified randomization, in that it is able to achieve balance over a large number of covariates when the sample size is small to medium. Despite its effectiveness, minimization has been questioned by regulatory agencies, mainly because of its increased complexity in practice and its potential impact on subsequent analysis. In recent years, however, with developments in clinical trials information technology, as well as advances in statistical theory, the attitudes toward minimization have evolved. In its 2013 draft guidelines, the European Medicines Agency (EMA) provided instructive guidelines for the implementation of minimization. In this paper, we review the broad class of methods that belong to minimization, including its original forms for balancing over covariate margins and its generalization to balancing over other subgroups of interest or over continuous covariates. Moreover, we review the theoretical development in recent years, including the large-sample properties of balance under minimization, the impact of minimization on inference for different data types, and on suitable randomization tests.

The pursuit of balance: An overview of covariate-adaptive randomization techniques in clinical trials (Source) 

Abstract: Randomization is fundamental to the design and conduct of clinical trials. Simple randomization ensures independence among subject treatment assignments and prevents potential selection biases, yet it does not guarantee balance in covariate distributions across treatment groups. Ensuring balance in important prognostic covariates across treatment groups is desirable for many reasons. A broad class of randomization methods for achieving balance are reviewed in this paper; these include block randomization, stratified randomization, minimization, and dynamic hierarchical randomization. Practical considerations arising from experience with using the techniques are described. A review of randomization methods used in practice in recent randomized clinical trials is also provided.

Allocation techniques for balance at baseline in cluster randomized trials: a methodological review (Source) 

Abstract: Reviews have repeatedly noted important methodological issues in the conduct and reporting of cluster randomized controlled trials (C-RCTs). These reviews usually focus on whether the intracluster correlation was explicitly considered in the design and analysis of the C-RCT. However, another important aspect requiring special attention in C-RCTs is the risk for imbalance of covariates at baseline. Imbalance of important covariates at baseline decreases statistical power and precision of the results. Imbalance also reduces face validity and credibility of the trial results. The risk of imbalance is elevated in C-RCTs compared to trials randomizing individuals because of the difficulties in recruiting clusters and the nested nature of correlated patient-level data. A variety of restricted randomization methods have been proposed as way to minimize risk of imbalance. However, there is little guidance regarding how to best restrict randomization for any given C-RCT. The advantages and limitations of different allocation techniques, including stratification, matching, minimization, and covariate-constrained randomization are reviewed as they pertain to C-RCTs to provide investigators with guidance for choosing the best allocation technique for their trial.

 A systematic review of randomisation method use in RCTs and association of trial design characteristics with method selection (Source) 

Abstract

Background: When conducting a randomised controlled trial, there exist many different methods to allocate participants, and a vast array of evidence-based opinions on which methods are the most effective at doing this, leading to differing use of these methods. There is also evidence that study characteristics affect the performance of these methods, but it is unknown whether the study design affects researchers’ decision when choosing a method.

Methods: We conducted a review of papers published in five journals in 2019 to assess which randomisation methods are most commonly being used, as well as identifying which aspects of study design, if any, are associated with the choice of randomisation method. Randomisation methodology use was compared with a similar review conducted in 2014.

Results: The most used randomisation method in this review is block stratification used in 162/330 trials. A combination of simple, randomisation, block randomisation, stratification and minimisation make up 318/330 trials, with only a small number of more novel methods being used, although this number has increased marginally since 2014. More complex methods such as stratification and minimisation seem to be used in larger multicentre studies.

Conclusions: Within this review, most methods used can be classified using a combination of simple, block stratification and minimisation, suggesting that there is not much if any increase in the uptake of newer more novel methods. There seems to be a noticeable polarisation of method use, with an increase in the use of simple methods, but an increase in the complexity of more complex methods, with greater numbers of variables included in the analysis, and a greater number of strata.

An evaluation of constrained randomization for the design and analysis of group-randomized trials (Source) 

Abstract: In group-randomized trials, a frequent practical limitation to adopting rigorous research designs is that only a small number of groups may be available, and therefore simple randomization cannot be relied upon to balance key group-level prognostic factors across the comparison arms. Constrained randomization is an allocation technique proposed for ensuring balance, and can be used together with a permutation test for randomization-based inference. However, several statistical issues have not been thoroughly studied when constrained randomization is considered. Therefore, we used simulations to evaluate key issues including: the impact of the choice of the candidate set size and the balance metric used to guide randomization; the choice of adjusted versus unadjusted analysis; and the use of model-based versus randomization-based tests. We conducted a simulation study to compare the type I error and power of the F-test and the permutation test in the presence of group-level potential confounders. Our results indicate that the adjusted F-test and the permutation test perform similarly and slightly better for constrained randomization relative to simple randomization in terms of power, and the candidate set size does not substantially affect their power. Under constrained randomization, however, the unadjusted F-test is conservative while the unadjusted permutation test carries the desired type I error rate as long as the candidate set size is not too small; the unadjusted permutation test is consistently more powerful than the unadjusted F-test, and gains power as candidate set size changes. Finally, we caution against the inappropriate specification of permutation distribution under constrained randomization. An ongoing group-randomized trial is used as an illustrative example for the constrained randomization design.

An evaluation of constrained randomization for the design and analysis of group-randomized trials with binary outcomes (Source) 

Abstract: In group-randomized trials, a frequent practical limitation to adopting rigorous research designs is that only a small number of groups may be available, and therefore simple randomization cannot be relied upon to balance key group-level prognostic factors across the comparison arms. Constrained randomization is an allocation technique proposed for ensuring balance, and can be used together with a permutation test for randomization-based inference. However, several statistical issues have not been thoroughly studied when constrained randomization is considered. Therefore, we used simulations to evaluate key issues including: the impact of the choice of the candidate set size and the balance metric used to guide randomization; the choice of adjusted versus unadjusted analysis; and the use of model-based versus randomization-based tests. We conducted a simulation study to compare the type I error and power of the F-test and the permutation test in the presence of group-level potential confounders. Our results indicate that the adjusted F-test and the permutation test perform similarly and slightly better for constrained randomization relative to simple randomization in terms of power, and the candidate set size does not substantially affect their power. Under constrained randomization, however, the unadjusted F-test is conservative while the unadjusted permutation test carries the desired type I error rate as long as the candidate set size is not too small; the unadjusted permutation test is consistently more powerful than the unadjusted F-test, and gains power as candidate set size changes. Finally, we caution against the inappropriate specification of permutation distribution under constrained randomization. An ongoing group-randomized trial is used as an illustrative example for the constrained randomization design.