Evidence map›Paper›PMID 37805939›Full record

ArticleBiostatistics (Oxford, England)2024

Joint modeling in presence of informative censoring on the retrospective time scale with application to palliative care research.

Quran Wu, Michael Daniels, Areej El-Jawahri, Marie Bakitas, Zhigang Li

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biostatistics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Quran WuDepartment of Biostatistics, 2004 Mowry Rd, University of Florida, Gainesville, FL, 32610, USA.
Michael DanielsDepartment of Statistics, 102 Griffin-Floyd Hall, University of Florida, Gainesville, FL, 32611, USA.
Areej El-JawahriDepartment of Oncology, Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02114, USA.
Marie BakitasSchool of Nursing, University of Alabama at Birmingham, 1720 2nd Avenue South, Birmingham, AL, 35294, USA.
Zhigang LiDepartment of Biostatistics, 2004 Mowry Rd, University of Florida, Gainesville, FL, 32610, USA.

Funding

Combining longitudinal cohort studies to examine cardiovascular risk factor trajectories across the adult lifespan and their association with diseaseR01HL158963 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI DANIELS, MICHAEL J, SIDDIQUE, JUNED · 2021 to 2024
$2.3M
Mediation Analysis Methods to Model Human Microbiome Mediating Disease-Leading Causal Pathways in ChildrenR01GM123014 · NIGMS · UNIVERSITY OF FLORIDA · PI LI, ZHIGANG · 2017 to 2021
$2.1M
Bayesian machine learning for complex missing data and causal inference with a focus on cardiovascular and obesity studiesR01HL166324 · NHLBI · UNIVERSITY OF FLORIDA · PI Michael J Daniels · 2023 to 2026
$2.1M
Early vs. Later Palliative Cancer Care: Clinical and Biobehavioral EffectsR01NR011871 · NINR · DARTMOUTH COLLEGE · PI BAKITAS, MARIE ANNE · 2009 to 2011
$1.4M
NHLBI NIH HHS R01 HL158963NHLBI NIH HHS R01 HL166324NIGMS NIH HHS R01 GM123014NIH HHS R01GM123014NINR NIH HHS R01 NR011871
6 · The paper itself

Abstract

Joint modeling of longitudinal data such as quality of life data and survival data is important for palliative care researchers to draw efficient inferences because it can account for the associations between those two types of data. Modeling quality of life on a retrospective from death time scale is useful for investigators to interpret the analysis results of palliative care studies which have relatively short life expectancies. However, informative censoring remains a complex challenge for modeling quality of life on the retrospective time scale although it has been addressed for joint models on the prospective time scale. To fill this gap, we develop a novel joint modeling approach that can address the challenge by allowing informative censoring events to be dependent on patients' quality of life and survival through a random effect. There are two sub-models in our approach: a linear mixed effect model for the longitudinal quality of life and a competing-risk model for the death time and dropout time that share the same random effect as the longitudinal model. Our approach can provide unbiased estimates for parameters of interest by appropriately modeling the informative censoring time. Model performance is assessed with a simulation study and compared with existing approaches. A real-world study is presented to illustrate the application of the new approach.

Indexed as

Models, StatisticalPalliative CareQuality of LifeHumansLongitudinal StudiesRetrospective StudiesSurvival AnalysisFrailty modelJoint modelingLongitudinal data analysisPalliative careSemi-parametric

Identifiers

PMID37805939
PMCPMC11247190

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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.