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.
Repeated measurement quantitative data is a common data type in clinical studies, and is frequently utilized to assess the therapeutic effects of the intervention measures at a single time point in clinical trials. This study clarifies the concepts and calculation methods for sample size estimation of repeated measurement quantitative data, in order to explore the research question of "comparing group differences at a single time point", from three perspectives: the primary research questions in clinical studies, the main statistical analysis methods and the definitions of the primary outcome indicators. Discrepancies in sample sizes calculated by various methods under different correlation coefficients and varying numbers of repeated measurements were examined. The study revealed that the sample size calculation method based on the mixed-effects model or generalized estimating equations (GEE) accounts for both the correlation coefficient and the number of repeated measurements, resulting in the smallest estimated sample size. Secondly, the sample size calculation method based on covariance analysis considers the correlation coefficient and produces a smaller estimated sample size than the t-test. The t-test based sample size calculation method requires an appropriate approach to be selected according to the definition of the primary outcome measure. The alignment between the sample size calculation method, the statistical analysis method and the definition of the primary outcome measure is essential to avoid the risk of overestimation or underestimation of the required sample size.