ArticleStatistics in medicine2020
Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: A pseudo-observation approach.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Dynamic Prediction of Clinical Endpoints in Survival Analysis With Machine and Deep Learning: A Systematic Review and Regulatory Perspective.CPT: pharmacometrics & systems pharmacology · 2026Pooled it
- Blood biomarker trajectories in ICU-directed prediction models - A scoping review.The EPMA journal · 2026Review
- Joint modeling of longitudinal and time-to-event data for dynamic disease risk prediction using proteomics.Protein science : a publication of the Protein Society · 2026Article
- Impact of Disease-Specific Treatment and Non-Selective Beta Blockers on Risk of PVT in Cirrhotic Patients With HCV or PBC.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Antiviral treatment and statin use and risk of decompensated cirrhosis in patients with hepatitis C: a time-dependent analysis of a retrospective cohort study.BMJ open gastroenterology · 2026Article
- Machine learning analysis of s-EASIX for predicting 30-day mortality in sepsis patients from MIMIC-IV.Scientific reports · 2026Article
- Mass Spectrometry-Based Metabolomics in Pediatric Health and Disease.Metabolites · 2026Review
- Redefining cardiometabolic biomarkers in the big data era: toward a personalized medicine-centered reconstruction of risk prediction models.Frontiers in cardiovascular medicine · 2026Article
- Dynamic and concordance-assisted learning for risk stratification with application to Alzheimer's disease.Biostatistics (Oxford, England) · 2025Article
- Dynamic survival analysis via a landmarking-gradient boosting approach and its application to kidney transplant data.BMC medical informatics and decision making · 2025Article
- Early dynamic changes in platelet counts and 28-day mortality in sepsis patients: a retrospective cohort study using dynamic latent class model and generalized additive mixture model analysis.Frontiers in medicine · 2025Article
- Correspondence to editorial on "Hepatocellular carcinoma prediction model performance decreases with long-term antiviral therapy in chronic hepatitis B patients".Clinical and molecular hepatology · 2024Article
- Subphenotypes of platelet count trajectories in sepsis from multi-center ICU data.Scientific reports · 2024Observational
- Association of inflammatory trajectory with subarachnoid hemorrhage mortality.Neurosurgical review · 2024Article
- Pseudo-value regression trees.Lifetime data analysis · 2024Article
- Dynamic risk prediction of survival in liver cirrhosis: A comparison of landmarking approaches.PloS one · 2024Article
- Developing predictive precision medicine models by exploiting real-world data using machine learning methods.Journal of applied statistics · 2024Article
- Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach.BMC medical research methodology · 2022Article
- Association of longitudinal platelet count trajectory with ICU mortality: A multi-cohort study.Frontiers in immunology · 2022Article
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4 authors.
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Abstract
Longitudinal biomarker data are often collected in studies, providing important information regarding the probability of an outcome of interest occurring at a future time. With many new and evolving technologies for biomarker discovery, the number of biomarker measurements available for analysis of disease progression has increased dramatically. A large amount of data provides a more complete picture of a patient's disease progression, potentially allowing us to make more accurate and reliable predictions, but the magnitude of available data introduces challenges to most statistical analysts. Existing approaches suffer immensely from the curse of dimensionality. In this article, we propose methods for making dynamic risk predictions using repeatedly measured biomarkers of a large dimension, including cases when the number of biomarkers is close to the sample size. The proposed methods are computationally simple, yet sufficiently flexible to capture complex relationships between longitudinal biomarkers and potentially censored events times. The proposed approaches are evaluated by extensive simulation studies and are further illustrated by an application to a data set from the Nephrotic Syndrome Study Network.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.