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.
Clinical prediction models refer to models that can predict the probability of the occurrence of a certain clinical outcome event of the research objects, and they have important value in fields such as disease risk stratification, prognosis prediction, and precision medical decision - making. To further standardize this methodology, in 2024, an international multidisciplinary expert group composed of institutions from Switzerland, the Netherlands, the United Kingdom, and others, based on the TRIPOD statement and the PROBAST assessment tool, jointly released the "Step - by - step guide for developing clinical prediction models". This guide systematically constructs 13 steps: defining the objective, creating a team, conducting a literature review, developing a protocol, choosing to develop a new model or update an existing model, defining the outcome measure, identifying candidate predictors, collecting and checking data, determining the sample size, handling missing data, fitting the prediction model, evaluating the performance of the prediction model, determining the final model, performing decision curve analysis, evaluating the predictive ability of individual predictors, writing a report and publishing the results. This paper deeply analyzes the steps of this guide, aiming to provide a reference for clinical researchers.
