ArticleStatistics in medicine2016
Bayesian joint ordinal and survival modeling for breast cancer risk assessment.
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- Longitudinal evaluation of serial ctDNA kinetics using joint modeling in patients with metastatic breast cancer.NPJ precision oncology · 2026Article
- The DeepJoint Algorithm: An Innovative Approach for Studying the Longitudinal Evolution of Quantitative Mammographic Density and Its Association With Screen-Detected Breast Cancer Risk.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Development and evaluation of a method to assess breast cancer risk using a longitudinal history of mammographic density: a cohort study.Breast cancer research : BCR · 2023Article
- Studying the association between longitudinal mammographic density measurements and breast cancer risk: a joint modelling approach.Breast cancer research : BCR · 2023Article
- Modeling unmeasured baseline information in observational time-to-event data subject to delayed study entry.Statistical methods in medical research · 2023Article
- Developing and validating an individualized breast cancer risk prediction model for women attending breast cancer screening.PloS one · 2021Article
- Bayesian joint modelling of longitudinal and time to event data: a methodological review.BMC medical research methodology · 2020Review
- Estimating Risk of Breast Cancer Occurrences at Different Ages: Application of Survival TechniquesAsian Pacific journal of cancer prevention : APJCP · 2018Article
- Bayesian joint ordinal and survival modeling for breast cancer risk assessment.Statistics in medicine · 2016Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model. Time-to-event is modeled through a left-truncated proportional-hazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects. General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the assessment of the impact of the baseline covariates and the longitudinal marker on the hazard function. The flexibility provided by the joint model makes possible to dynamically estimate individual event-free probabilities and predict future longitudinal marker values. The model is applied to the assessment of breast cancer risk in women attending a population-based screening program. The longitudinal ordinal marker is mammographic breast density measured with the Breast Imaging Reporting and Data System (BI-RADS) scale in biennial screening exams. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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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.