The risk prediction model (RPM) can be used to predict the risks of disease for individuals, playing an extremely important role in decision-making regarding disease prevention, treatment, and prognostic management. Most of the existing RPMs only utilize a single-time cross-section of variable data, so-called static models, which fail to consider the many changes during disease progression and lead to limited prediction accuracy. Dynamic prediction models can incorporate longitudinal data such as repeated measurements of variables during follow-up to capture the longitudinal changes in individual characteristics over time, describe the dynamic trajectory of individual disease risk and improve the prediction accuracy of the models; however, their application in medical research is still relatively small. In this paper, we conducted a systematic literature search to summarize the commonly used dynamic models: joint model, landmark model, and Bayesian dynamic model. By introducing their application scenarios, advantages and disadvantages, and software implementations and conducting comparisons, we aimed to provide methodological references for the future application of dynamic prediction models in medical research.
