Minimal sufficient balance—a new strategy to balance baseline covariates and preserve randomness of treatment allocation

Abstract

In many clinical trials, baseline covariates could affect the primary outcome. Commonly used strategies to balance baseline covariates include stratified constrained randomization and minimization. Stratification is limited to few categorical covariates. Minimization lacks the randomness of treatment allocation. Both apply only to categorical covariates. As a result, serious imbalances could occur in important baseline covariates not included in the randomization algorithm. Furthermore, randomness of treatment allocation could be significantly compromised because of the high proportion of deterministic assignments associated with stratified block randomization and minimization, potentially resulting in selection bias. Serious baseline covariate imbalances and selection biases often contribute to controversial interpretation of the trial results. The National Institute of Neurological Disorders and Stroke recombinant tissue plasminogen activator Stroke Trial and the Captopril Prevention Project are two examples. In this article, we propose a new randomization strategy, termed the minimal sufficient balance randomization, which will dually prevent serious imbalances in all important baseline covariates, including both categorical and continuous types, and preserve the randomness of treatment allocation. Computer simulations are conducted using the data from the National Institute of Neurological Disorders and Stroke recombinant tissue plasminogen activator Stroke Trial. Serious imbalances in four continuous and one categorical covariate are prevented with a small cost in treatment allocation randomness. A scenario of simultaneously balancing 11 baseline covariates is explored with similar promising results. The proposed minimal sufficient balance randomization algorithm can be easily implemented in computerized central randomization systems for large multicenter trials.

Comparison of statistical and operational properties of subject randomization procedures for large multicenter clinical trial treating medical emergencies

“…The simulation shows that the minimization method works better than the hierarchical biased coin design. Minimization method has been proven as the most effective treatment allocation algorithm for simultaneously balancing multiple baseline covariates in multicenter clinical trials [25, 26]. However, the high proportion of deterministic assignments remains a serious concern as it may lead to treatment allocation concealment failure and potential selection bias [21, 25].…”

“…However, the high proportion of deterministic assignments remains a serious concern as it may lead to treatment allocation concealment failure and potential selection bias [21, 25]. Furthermore, the necessity of strict imbalance control in randomized controlled large multicenter clinical trials is highly questionable [26]. To alleviate these two problems, we can consider a modified version of the minimization method with an imbalance control threshold δ and a biased coin probability p bc .…”

Improving early clinical trial phase identification of promising therapeutics

“…54,55 When used in large trials such as IST-3, overall balance is achievable, but correction methods were still necessary for subgroup analysis. Adaptive designs include minimization routines that identify key prognostic factors to guide treatment assignments.…”

The role of technology in avoiding bias in the design and execution of clinical trials

“…Intervention effect estimates were exaggerated when there was inadequate allocation concealment in trials where a subjective outcome was analyzed, but there was little evidence of bias in trials with objective outcomes. 5 Zhao et al 6 describe the Captopril Prevention Project (CAPP) trial, which is an example of how selection bias can occur when technology is not used for the implementation of randomization. The purpose of the CAPP trial was to compare the treatment of hypertension by an angiotensin-convertingenzyme inhibitor (captopril) with the conventional therapy.…”