ObjectiveTo introduce the theoretical foundation, application scenarios, and implementation in R language of covariate-adaptive randomization (CAR) and restricted randomization (RR). MethodsThis article initially expounds the significance of CAR and RR in clinical trials, particularly in balancing covariates between treatment groups on the basis of dynamic adjustment and pre-defined rules, in order to enhance the accuracy and reliability of trial outcomes. ResultsRR is applicable to large-scale trials, ensuring balance between groups but potentially inducing selection bias; CAR is suitable for small-sample and complex covariate trials, improving accuracy yet having complex implementation. In clinical trials of traditional Chinese medicine, CAR enables personalized group allocation, and RR ensures baseline balance. Dynamic randomization strategies enhance the flexibility of trials. ConclusionThrough code examples in R language, this study offers practical guidance for researchers to implement these randomization methods, ensuring the scientificity and rigor of data processing and analysis.
Randomized controlled trial has been the "gold standard" for clinical trials, in which randomization serves as a fundamental principle of clinical trials and plays an important role in balancing covariates. The allocation probability in traditional design is fixed, while that in adaptive randomization can alter during the experiment according to the specified plan to achieve the purposes of balancing the sample size, maximizing the benefit of patient, or balancing covariates etc. In this study, the adaptive randomization methods applied in clinical trials are discussed to explore their advantages and disadvantages for providing reference for the randomization of clinical trials.
