ArticleStatistical modelling2021
Assessing Importance of Biomarkers: a Bayesian Joint Modeling Approach of Longitudinal and Survival Data with Semicompeting Risks.
Article in Statistical modelling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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
3 citing papers in PubMed.
- A multistate transition model for survival estimation in randomized trials with treatment switching and a cured subgroup.BMC medical research methodology · 2025Article
- 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
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Longitudinal biomarkers such as patient-reported outcomes (PROs) and quality of life (QOL) are routinely collected in cancer clinical trials or other studies. Joint modeling of PRO/QOL and survival data can provide a comparative assessment of patient-reported changes in specific symptoms or global measures that correspond to changes in survival. Motivated by a head and neck cancer clinical trial, we develop a class of trajectory-based models for longitudinal and survival data with disease progression. Specifically, we propose a class of mixed effects regression models for longitudinal measures, a cure rate model for the disease progression time (
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Registered trials
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