ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2017
Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data with Applications to Cancer Clinical Trials.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Evaluating the application of dynamic prediction models in oncological prognostic studies with repeated measurement predictors.NPJ precision oncology · 2025Article
- Bayesian Modeling on Microbiome Data Analysis: Application to Subgingival Microbiome Study.Statistics in biosciences · 2024Article
- Imaging mediation analysis for longitudinal outcomes: a case study of childhood brain tumor survivorship.Statistics and its interface · 2024Article
- Bayesian Model Assessment for Jointly Modeling Multidimensional Response Data with Application to Computerized Testing.Psychometrika · 2022Article
- Bayesian criterion-based assessments of recurrent event models with applications to commercial truck driver behavior studies.Statistics in medicine · 2022Article
- Bayesian Design of Superiority Trials: Methods and Applications.Statistics in biopharmaceutical research · 2022Article
- BRIDGING RANDOMIZED CONTROLLED TRIALS AND SINGLE-ARM TRIALS USING COMMENSURATE PRIORS IN ARM-BASED NETWORK META-ANALYSIS.The annals of applied statistics · 2021Article
- Article
- Assessing Importance of Biomarkers: a Bayesian Joint Modeling Approach of Longitudinal and Survival Data with Semicompeting Risks.Statistical modelling · 2021Article
- Joint modelling of longitudinal and survival data in the presence of competing risks with applications to prostate cancer data.Statistical modelling · 2021Article
- Risk factor identification in cystic fibrosis by flexible hierarchical joint models.Statistical methods in medical research · 2021Article
- Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers.Biometrical journal. Biometrische Zeitschrift · 2020Article
- Bayesian Meta-Regression Model Using Heavy-Tailed Random-effects with Missing Sample Sizes for Self-thinning Meta-data.Statistics and its interface · 2020Article
- Bayesian flexible hierarchical skew heavy-tailed multivariate meta regression models for individual patient data with applications.Statistics and its interface · 2020Article
- Assessing the Relationship between Gestational Glycemic Control and Risk of Preterm Birth in Women with Type 1 Diabetes: A Joint Modeling Approach.Journal of diabetes research · 2020Article
- A new Bayesian joint model for longitudinal count data with many zeros, intermittent missingness, and dropout with applications to HIV prevention trials.Statistics in medicine · 2019Article
- Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington's disease.BMC medical research methodology · 2018Article
- Joint hierarchical Gaussian process model with application to personalized prediction in medical monitoring.Stat (International Statistical Institute) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Joint models for longitudinal and survival data are routinely used in clinical trials or other studies to assess a treatment effect while accounting for longitudinal measures such as patient-reported outcomes (PROs). In the Bayesian framework, the deviance information criterion (DIC) and the logarithm of the pseudo marginal likelihood (LPML) are two well-known Bayesian criteria for comparing joint models. However, these criteria do not provide separate assessments of each component of the joint model. In this paper, we develop a novel decomposition of DIC and LPML to assess the fit of the longitudinal and survival components of the joint model, separately. Based on this decomposition, we then propose new Bayesian model assessment criteria, namely, ΔDIC and ΔLPML, to determine the importance and contribution of the longitudinal (survival) data to the model fit of the survival (longitudinal) data. Moreover, we develop an efficient Monte Carlo method for computing the Conditional Predictive Ordinate (CPO) statistics in the joint modeling setting. A simulation study is conducted to examine the empirical performance of the proposed criteria and the proposed methodology is further applied to a case study in mesothelioma.
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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.