Sample size re-estimation (SSR) refers to the recalculation of the sample size using the existing trial data as original planned to ensure that the final statistical test achieved the pre-defined goals. SSR can enhance research efficiency, save trial costs, and accelerate the research process. Depending on whether the group assignment of the patients is known, SSR is divided into blinded sample size re-estimation and unblinded sample size re-estimation. Blinded sample size re-estimation can estimate the variance of the primary evaluation index through the EM algorithm or single sample variance re-estimation method, and then calculate the sample size. Unblinded sample size re-estimation can calculate the sample size by estimating the overall variance or therapeutic effect difference, but it needs to control the family wise type I error (FWER) rate. Cui-Hung-Wang method, conditional rejection probability method, P-value combination method, conditional error function, and promising zone are common methods used to control FWER. Currently, there are application examples of SSR methods. With the maturation of related theories and the popularization of methods, it is expected to be widely applied in clinical trials, especially in traditional Chinese medicine clinical trials in the future.
Response-adaptive randomization (RAR) dynamically adjusts the probability of assigning patients to different groups, optimizing treatment efficacy and participant welfare. It is particularly suitable for clinical studies involving multiple interventions or dose-finding and seamless phase II/III trials. This paper systematically introduces the concept, principles, and types of RAR, as well as its application in clinical trials (including traditional Chinese medicine research). It also provides R implementation code, offering researchers practical tools aimed at promoting the adoption of RAR in clinical practice.