The use of repeated measurement data from patients to improve the classification ability of prediction models is a key methodological issue in the current development of clinical prediction models. This study aims to investigate the statistical modeling approach of the two-stage model in developing prediction models for non-time-varying outcomes using repeated measurement data. Using the prediction of the risk of severe postpartum hemorrhage as a case study, this study presents the implementation process of the two-stage model from various perspectives, including data structure, basic principles, software utilization, and model evaluation, to provide methodological support for clinical investigators.
