Evaluating the application of dynamic prediction models in oncological prognostic studies with repeated measurement predictors

Recent cancer prognosis research emphasizes longitudinal data's importance for survival prediction, yet its analysis challenges researchers, often leading to oversimplified dual-timepoint comparisons (e.g., pre- vs. post-treatment). To meet precision oncology needs, this study evaluated dynamic prediction model (DPM) applications by a cross-sectional analysis of published studies. A comprehensive search of PubMed and Web of Science identified 6,549 records, from which 174 DPMs in 165 studies were analyzed. These studies covered 19 cancers and showed a rising trend in DPM usage (trend test, p < 0.001). Notably, 58.6% of studies used only one dynamic predictor. Seven DPM categories were identified: two-stage models (most common at 32.2%), joint models (28.2%), time-dependent covariate models (12.6%), multi-state models (10.3%), landmark Cox models (8.6%), artificial intelligence (4.6%), and others (3.4%). DPM distribution significantly shifted over 5 years (Chi-square test, p < 0.001), trending towards joint models and AI. We described and compared these DPMs across multiple dimensions, including principles, advantages and limitations, and clinical application scenarios. Joint models, integrating longitudinal and survival data, and artificial intelligence, extracting high-dimensional features, offer promising precision prognosis pathways. Future research should prioritize developing DPMs capable of handling high-dimensional data from smaller samples to improve treatment monitoring and prognosis.