ArticleStatistics in medicine2018
Exploring causality mechanism in the joint analysis of longitudinal and survival data.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
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Who cites it
12 citing papers in PubMed.
- Joint Analysis for Multivariate Longitudinal and Event Time Data With a Change Point Anchored at Interval-Censored Event Time.Statistics in medicine · 2026Article
- Quantifying Direct and Indirect Effects through Joint Modeling of Terminal Events and Gap Times between Recurrent Events.Journal of data science : JDS · 2026Article
- Validation of a Longitudinal Marker as a Surrogate Using Mediation Analysis and Joint Modeling: Evolution of the PSA as a Surrogate of the Disease-Free Survival.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Robust evaluation of longitudinal surrogate markers with censored data.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- High-dimensional mediation analysis for longitudinal mediators and survival outcomes.Briefings in bioinformatics · 2025Article
- Exploring causal mechanisms and quantifying direct and indirect effects using a joint modeling approach for recurrent and terminal events.Statistics in medicine · 2023Article
- Quantifying direct and indirect effect for longitudinal mediator and survival outcome using joint modeling approach.Biometrics · 2022Article
- Modeling the underlying biological processes in Alzheimer's disease using a multivariate competing risk joint model.Statistics in medicine · 2022Article
- Socioeconomic status and its relation with breast cancer recurrence and survival in young women in the Netherlands.Cancer epidemiology · 2022Article
- Causal mediation analysis between resistance exercise and reduced risk of cardiovascular disease based on the Aerobics Center Longitudinal Study.Journal of applied statistics · 2022Article
- Mediation analysis for survival data with high-dimensional mediators.Bioinformatics (Oxford, England) · 2021Article
- Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.Statistica Neerlandica · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In many biomedical studies, disease progress is monitored by a biomarker over time, eg, repeated measures of CD4 in AIDS and hemoglobin in end-stage renal disease patients. The endpoint of interest, eg, death or diagnosis of a specific disease, is correlated with the longitudinal biomarker. In this paper, we examine and compare different models of longitudinal and survival data to investigate causal mechanisms, specifically, those related to the role of random effects. We illustrate the methods by data from two clinical trials: an AIDS study and a liver cirrhosis study.
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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.