Journal of biomedical informatics · 2026
Predicting time-to-event clinical outcomes with multivariate repeated measurements of patient-level covariates: A systematic review.
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Cara Irene, Erdmann Frederik H, Attanasio Chiara, van 't Hof Nynke, Vidal-Itriago Andrés A, Geleijnse Gijs, van Laarhoven Hanneke W M, Verhoeven Rob H A
Аннотация
OBJECTIVE: Repeated measurements capture the progression of health over time and may inform survival prediction. The goal of this review is to identify and compare how the presented methodologies for survival prediction with repeated measurements have been applied to predict survival outcomes with multivariate structured repeated measurements and cluster them into families of methodologies.
METHODS: We performed a search on MEDLINE via Ovid, PubMed, Web of Science, and Embase. We included original peer-reviewed publications where a Time-To-Event (TTE) prediction model was developed, based on multivariate repeated measurements of health data. The protocol was registered in PROSPERO (CRD42024529572). The main outcome of interest was the strategy used for dealing with repeated measurements. We assigned a score to each methodology in terms of temporal modelling, ease of implementation, interpretability and explainability, flexibility, computational power, scalability, ability to deal with irregular sampling and dynamic prediction, based on the data extracted from the corresponding studies.
RESULTS: After screening 4029 records, we included 58 studies in the review. The studies were categorized based on the underlying survival models into traditional statistics and machine learning methodologies. In parallel, four families of methodologies to deal with repeated measurements were identified: joint models (statistics n = 18), deep learning based methods (machine learning n = 9), landmarking (statistics n = 13, machine learning n = 6) and data manipulation (statistics n = 3, machine learning n = 9). Statistical studies had a higher risk of bias, reported models trained on less longitudinal covariates and were based on smaller sample sizes, while machine learning could deal less often with irregular sampling of measurements. Joint models and deep learning were generally more suitable when dealing with complex temporal dependencies, while landmarking and data manipulation were simpler and computationally lighter options.
CONCLUSIONS: This systematic review identified and compared published studies that developed TTE prediction methodologies using repeated covariate measurements to predict clinical outcomes. Greater emphasis on external validation, transparent reporting, interpretability and explainability, and multimodal data integration is crucial to advance the field and enhance its impact on health outcomes.
METHODS: We performed a search on MEDLINE via Ovid, PubMed, Web of Science, and Embase. We included original peer-reviewed publications where a Time-To-Event (TTE) prediction model was developed, based on multivariate repeated measurements of health data. The protocol was registered in PROSPERO (CRD42024529572). The main outcome of interest was the strategy used for dealing with repeated measurements. We assigned a score to each methodology in terms of temporal modelling, ease of implementation, interpretability and explainability, flexibility, computational power, scalability, ability to deal with irregular sampling and dynamic prediction, based on the data extracted from the corresponding studies.
RESULTS: After screening 4029 records, we included 58 studies in the review. The studies were categorized based on the underlying survival models into traditional statistics and machine learning methodologies. In parallel, four families of methodologies to deal with repeated measurements were identified: joint models (statistics n = 18), deep learning based methods (machine learning n = 9), landmarking (statistics n = 13, machine learning n = 6) and data manipulation (statistics n = 3, machine learning n = 9). Statistical studies had a higher risk of bias, reported models trained on less longitudinal covariates and were based on smaller sample sizes, while machine learning could deal less often with irregular sampling of measurements. Joint models and deep learning were generally more suitable when dealing with complex temporal dependencies, while landmarking and data manipulation were simpler and computationally lighter options.
CONCLUSIONS: This systematic review identified and compared published studies that developed TTE prediction methodologies using repeated covariate measurements to predict clinical outcomes. Greater emphasis on external validation, transparent reporting, interpretability and explainability, and multimodal data integration is crucial to advance the field and enhance its impact on health outcomes.