Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint

Adaptive Randomization Method to Prevent Extreme Instances of Group Size and Covariate Imbalance in Stroke Trials

BACKGROUND

A recent review of randomization methods used in large multicenter clinical trials within the National Institutes of Health Stroke Trials Network identified preservation of treatment allocation randomness, achievement of the desired group size balance between treatment groups, achievement of baseline covariate balance, and ease of implementation in practice as critical properties required for optimal randomization designs. Common-scale minimal sufficient balance (CS-MSB) adaptive randomization effectively controls for covariate imbalance between treatment groups while preserving allocation randomness but does not balance group sizes. This study extends the CS-MSB adaptive randomization method to achieve both group size and covariate balance while preserving allocation randomness in hyperacute stroke trials.

METHODS

A full factorial in silico simulation study evaluated the performance of the proposed new CSSize-MSB adaptive randomization method in achieving group size balance, covariate balance, and allocation randomness compared with the original CS-MSB method. Data from 4 existing hyperacute stroke trials were used to investigate the performance of CSSize-MSB for a range of sample sizes and covariate numbers and types. A discrete-event simulation model created with AnyLogic was used to dynamically visualize the decision logic of the CSSize-MSB randomization process for communication with clinicians.

RESULTS

The proposed new CSSize-MSB algorithm uniformly outperformed the CS-MSB algorithm in controlling for group size imbalance while maintaining comparable levels of covariate balance and allocation randomness in hyperacute stroke trials. This improvement was consistent across a distribution of simulated trials with varying levels of imbalance but was increasingly pronounced for trials with extreme cases of imbalance. The results were consistent across a range of trial data sets of different sizes and covariate numbers and types.

CONCLUSIONS

The proposed adaptive CSSize-MSB algorithm successfully controls for group size imbalance in hyperacute stroke trials under various settings, and its logic can be readily explained to clinicians using dynamic visualization.

How to Balance Prognostic Factors in Controlled Phase II Trials: Stratified Permuted Block Randomization or Minimization? An Analysis of Clinical Trials in Digestive Oncology

In controlled phase II trials, major prognostic factors need to be well balanced between arms. The main procedures used are SPBR (Stratified Permuted Block Randomization) and minimization. First, we provide a systematic review of the treatment allocation procedure used in gastrointestinal oncology controlled phase II trials published in 2019. Second, we performed simulations using data from six phase II studies to measure the impacts of imbalances and bias on the efficacy estimations. From the 40 articles analyzed, all mentioned randomization in both the title and abstract, the median number of patients included was 109, and 77.5% were multicenter. Of the 27 studies that reported at least one stratification variable, 10 included the center as a stratification variable, 10 used minimization, 9 used SBR, and 8 were unspecified. In real data studies, the imbalance increased with the number of centers. The total and marginal imbalances were higher with SBR than with minimization, and the difference increased with the number of centers. The efficiency estimates per arm were close to the original trial estimate in both procedures. Minimization is often used in cases of numerous centers and guarantees better similarity between arms for stratification variables for total and marginal imbalances in phase II trials.

Comparison of Pocock and Simon's covariate-adaptive randomization procedures in clinical trials

When multiple influential covariates need to be balanced during a clinical trial, stratified blocked randomization and covariate-adaptive randomization procedures are frequently used in trials to prevent bias and enhance the validity of data analysis results. The latter approach is increasingly used in practice for a study with multiple covariates and limited sample sizes. Among a group of these approaches, the covariate-adaptive procedures proposed by Pocock and Simon are straightforward to be utilized in practice. We aim to investigate the optimal design parameters for the patient treatment assignment probability of their developed three methods. In addition, we seek to answer the question related to the randomization performance when additional covariates are added to the existing randomization procedure. We conducted extensive simulation studies to address these practically important questions.

Digital teaching tools in sports medicine: A randomized control trial comparing the effectiveness of virtual seminar and virtual fishbowl teaching method in medical students

Background

Since the COVID-19 pandemic, the demand for online courses has increased enormously. Therefore, finding new methods to improve medical education is imperative.

Objective

The aim of this study was to compare the self-reports of the individual student-centered virtual teaching techniques (seminar versus fishbowl) in a group of medical students.

Methods

During the second semester of 2020, students in the clinical phase of the study (n = 144) participated in the optional subject of Sports Medicine. The students were divided into 2 groups. One group (n = 72) received the knowledge transfer in the form of a virtual seminar, the other group (n = 72) in the form of a virtual fishbowl.

Results

Virtual seminar and virtual fishbowl students gave insights into these teaching techniques. Most of the students from the virtual fishbowl group believed that the virtual fishbowl format allowed them to be more actively involved in learning. The mean quiz scores were statistically higher for students in the virtual fishbowl group than students in the virtual seminar group (p < 0. 001).

Conclusion

This study concluded that virtual seminars and virtual fishbowl formats could be served as structured learning and teaching formats. At the same time, the virtual fishbowl format can promote an active exchange of knowledge from students’ perspectives.