Introduction Platform trials have gained prominence, particularly during the COVID-19 pandemic, due to their flexibility and efficiency in evaluating multiple interventions. However, their complex design introduces methodological and regulatory challenges, especially in randomisation when adding new treatment arms. Method This study uses simulations based on a real platform trial to assess the performance of different randomisation methods: simple randomisation, stratified block randomisation, stratified block urn design, and minimisation. We evaluate these methods under various allocation ratios, focusing on covariate balance, allocation predictability, and allocation accuracy, particularly for newly added arms. We also examine the trade-off between covariate balance and allocation predictability. Results This study highlights the inherent trade-off between achieving good covariate balance and maintaining adequate allocation unpredictability. SBUD slightly outperforms SBR for certain stage 2 allocation ratios, though not consistently across all scenarios. Minimisation achieves the best covariate balance among all methods but at the cost of reduced randomness. Allocating more patients exclusively to the newly added arm does not effectively reduce covariate imbalance; however, allocating more patients to both the control and new arms yields better balance. Additionally, incorporating non-concurrent data in the minimisation process improves covariate balance compared to using only concurrent data. Conclusion The findings suggest that when the primary goal is to achieve covariate balance, especially for new arms, trialists should consider using minimisation with non-concurrent data and allocate patients proportionately to control and new arms. Balancing covariate distribution while maintaining sufficient randomness is crucial to ensuring the methodological robustness and reliability of platform trials.
Azher et al. (Wed,) studied this question.