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.
The covariate-adjusted response-adaptive randomisation (CARA) design combines the advantages of response-adaptive randomisation and covariate-adaptive randomisation, and improves the efficiency and reliability of clinical trials by combining analytical results and covariates and dynamically adjusting the allocation of subsequent patients. This paper describes in detail several methods of CARA design and their example applications of various methods, including the dominant confidence method, the urn model, the generalized linear model, and the Atkinson model, and provides the corresponding R codes in anticipation of a wider application of the provided R codes in clinical trials.