ArticleStatistics in medicine2016
Prediction of coronary artery disease risk based on multiple longitudinal biomarkers.
Article in Statistics in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis.American journal of epidemiology · 2017Pooled it
- Breaking binary in cardiovascular disease risk prediction.NPJ cardiovascular health · 2025Review
- Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.CNS neuroscience & therapeutics · 2024Article
- Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study.BMC medical research methodology · 2023Observational
- Modelling of longitudinal data to predict cardiovascular disease risk: a methodological review.BMC medical research methodology · 2021Article
- Multivariate Longitudinal Assessment of Kidney Function Outcomes on Graft Survival after Kidney Transplantation Using Multivariate Joint Modeling Approach: A Retrospective Cohort Study.Iranian journal of medical sciences · 2021Article
- Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers.Biometrical journal. Biometrische Zeitschrift · 2020Article
- Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods.Diagnostic and prognostic research · 2020Review
- Estimating the association between blood pressure variability and cardiovascular disease: An application using the ARIC Study.Statistics in medicine · 2019Article
- Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer's disease.Statistical methods in medical research · 2019Article
- The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study.Statistics in medicine · 2017Article
- Functional joint model for longitudinal and time-to-event data: an application to Alzheimer's disease.Statistics in medicine · 2017Article
- DYNAMIC PREDICTION FOR MULTIPLE REPEATED MEASURES AND EVENT TIME DATA: AN APPLICATION TO PARKINSON'S DISEASE.The annals of applied statistics · 2017Article
- Dynamics of biomarkers in relation to aging and mortality.Mechanisms of ageing and development · 2016Review
- Joint Models for Multiple Longitudinal Processes and Time-to-event Outcome.Journal of statistical computation and simulation · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In the last decade, few topics in the area of cardiovascular disease (CVD) research have received as much attention as risk prediction. One of the well-documented risk factors for CVD is high blood pressure (BP). Traditional CVD risk prediction models consider BP levels measured at a single time and such models form the basis for current clinical guidelines for CVD prevention. However, in clinical practice, BP levels are often observed and recorded in a longitudinal fashion. Information on BP trajectories can be powerful predictors for CVD events. We consider joint modeling of time to coronary artery disease and individual longitudinal measures of systolic and diastolic BPs in a primary care cohort with up to 20 years of follow-up. We applied novel prediction metrics to assess the predictive performance of joint models. Predictive performances of proposed joint models and other models were assessed via simulations and illustrated using the primary care cohort.
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Registered trials
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.