ArticleAnnals of translational medicine2021
Development and application of a dynamic prediction model for esophageal cancer.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Dynamic prediction of adverse outcomes in discharged COPD patients using patient-reported outcomes.BMC medical research methodology · 2026Observational
- Personalized prognostic model for colorectal cancer in the era of precision medicine: a dynamic approach based on real-world data.International journal of clinical oncology · 2025Article
- The reporting quality and methodological quality of dynamic prediction models for cancer prognosis.BMC medical research methodology · 2025Article
- Assessing biomarker trajectories for mortality risk in peritoneal dialysis: A focus on multivariate joint modeling.PloS one · 2025Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrent prediction models of esophageal cancer (EC) are limited to predicting at a specific time point, and ignore changes in hazard ratios of predictive variables, known as time-varying effects. Our study aimed to investigate variables with time-varying effects in EC and to develop a prediction model that can update the 5-year predicted dynamic overall survival (DOS) probability during the follow-up period.
methodsFirstly, the clinicopathological information and survival data of 4,541 patients with EC was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2007 and 2011 for modeling. Secondly, the time-varying effect of variables was assessed and the dynamic prediction model was developed based on the proportional baselines landmark supermodel.
resultsHere, we found that age at diagnosis, sex, location of primary tumor, histological type, chemotherapy, surgery, and T stage showed significant time-varying effects on overall survival. Thirdly, the prediction model was validated by an internal SEER validation cohort and a Chinese patient cohort, respectively, and achieved promising results as follows: area under the curve (AUC) =0.733 (internal validation) and 0.864 (external validation). The heuristic shrinkage factor was 0.995. Finally, several clear cases were selected as examples for model application to map the patient's 5-year DOS curves and to respectively demonstrate the impact of different variables' time-varying effect on survival.
conclusionsOverall, our results suggest that the existence of time-varying effect highlights the importance of updating the predicted survival probability during the follow-up period. Moreover, this prediction model can be used to assist doctors in making more-individualized treatment decisions based on a dynamic assessment of patient prognosis.
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