SynthesisBMC medical research methodology2026
Joint modelling on clinical trials in clinical journals: a systematic review.
Synthesis in BMC medical research methodology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- Decoding the temporal dimension of innate immunity in intravesical BCG therapy.Frontiers in immunology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundJoint modelling (JM) provides an advanced statistical framework for the simultaneous analysis of longitudinal data and time-to-event outcomes. This review aims to assess clinical findings generated by JM in clinical trials and to identify common applications of JM within these studies.
methodsWe systematically searched electronic databases to identify clinical trial reports that applied JM and were published in clinical journals. From each report, we extracted information on JM applications and summarized the key characteristics of the studies.
resultsWe identified 44 eligible clinical trials. Most studies used JM to evaluate how time-to-event outcomes were adjusted, mediated, or predicted based on longitudinal data, such as health-related quality of life or biomarkers. Six studies addressed methodological adjustments for missing-not-at-random data, informative dropout, and bias arising from competing risks. Additionally, three studies employing Bayesian JM demonstrated how complex models facilitated multivariate and dynamic predictions of survival benefits.
conclusionsClinical application of JM remains limited, although statistical methodologies for JM have advanced considerably. Progress in translating JM into clinical research practice has been modest. To support its effective use, clinical researchers require a deeper understanding of how to interpret outcomes generated by JM and how to integrate them into an evidence-based framework.
Indexed as
Identifiers
What OpenQuestion holds
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.