Evidence map›Paper›PMID 39081111›Full record

ArticleJournal of the Royal Society, Interface2024

Individualized survival predictions using state space model with longitudinal and survival data.

Mark Cauchi, Andrew R Mills, Allan Lawrie, David G Kiely, Visakan Kadirkamanathan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Society, Interface, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mark CauchiDepartment of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.ORCID 0000-0001-9023-8028
Andrew R MillsDepartment of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.
Allan LawrieNational Heart and Lung Institute, Imperial College London, Dovehouse Street, London SW3 6LY, UK.
David G KielySheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital Sheffield, NIHR Biomedical Research Centre Sheffield and Department of Clinical Medicine, The University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK.
Visakan KadirkamanathanDepartment of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monitoring disease progression often involves tracking biomarker measurements over time. Joint models (JMs) for longitudinal and survival data provide a framework to explore the relationship between time-varying biomarkers and patients' event outcomes, offering the potential for personalized survival predictions. In this article, we introduce the linear state space dynamic survival model for handling longitudinal and survival data. This model enhances the traditional linear Gaussian state space model by including survival data. It differs from the conventional JMs by offering an alternative interpretation via differential or difference equations, eliminating the need for creating a design matrix. To showcase the model's effectiveness, we conduct a simulation case study, emphasizing its performance under conditions of limited observed measurements. We also apply the proposed model to a dataset of pulmonary arterial hypertension patients, demonstrating its potential for enhanced survival predictions when compared with conventional risk scores.

Indexed as

Models, StatisticalHumansLongitudinal StudiesSurvival Analysisexpectation maximization algorithmjoint modellongitudinal datapulmonary arterial hypertensionstate space modelsurvival data

Identifiers

PMID39081111
PMCPMC11289657

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.