Evidence map›Paper›PMID 42337851›Full record

ArticleStatistical methods in medical research2026

Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.

Zifang Kong, Sy Han Chiou, Naim M Maalouf, Yu-Lun Liu

Abstract read
In one paragraph

Article in Statistical methods in medical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Zifang KongDepartment of Statistics and Data Science, Southern Methodist University, Dallas, TX, United States.
Sy Han ChiouDepartment of Statistics and Data Science, Southern Methodist University, Dallas, TX, United States.
Naim M MaaloufDepartment of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States.
Yu-Lun LiuPeter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, United States.ORCID 0000-0003-3541-1814

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recurrent health events often involve complex inter-relationships between longitudinal biomarkers and time-to-event outcomes, further complicated by sparse, irregular data collection and time-dependent correlations among events. Traditional statistical methods frequently struggle with these complexities, resulting in biased estimates and suboptimal modeling performance. To address these challenges, we propose the

Indexed as

Models, StatisticalBayes TheoremComputer SimulationHumansLongitudinal StudiesPrincipal Component AnalysisRecurrenceRegression Analysisautoregressive frailty modeldynamic predictionfunctional principal component analysisgap time modelrecurrent eventssparse longitudinal data analysis

Identifiers

PMID42337851
PMCPMC13342473

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Registered trials

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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.