Evidence map›Paper›PMID 38172348›Full record

ArticleEuropean journal of clinical nutrition2024

Detecting potential outliers in longitudinal data with time-dependent covariates.

Lazarus K Mramba, Xiang Liu, Kristian F Lynch, Jimin Yang, Carin Andrén Aronsson, Sandra Hummel, Jill M Norris, Suvi M Virtanen, Leena Hakola, Ulla M Uusitalo and 1 more

Abstract read
In one paragraph

Article in European journal of clinical nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Observational
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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

11 authors.

Lazarus K MrambaHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA. Lazarus.Mramba@epi.usf.edu.ORCID http://orcid.org/0000-0003-3456-769X
Xiang LiuHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Kristian F LynchHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Jimin YangHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.ORCID http://orcid.org/0000-0002-9829-6916
Carin Andrén AronssonDepartment of Clinical Sciences, Lund University, Malmö, Sweden.ORCID http://orcid.org/0000-0003-0256-9367
Sandra HummelInstitute of Diabetes Research, Helmholtz Zentrum and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität and Forschergruppe Diabetes e.V, Munich, Germany.
Jill M NorrisDepartment of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Suvi M VirtanenFinnish Institute for Health and Welfare, Health and Well-Being Promotion Unit, Helsinki, Finland.
Leena HakolaFaculty of Social Sciences, Unit of Health Sciences, Tampere University, Tampere, Finland.
Ulla M UusitaloHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.ORCID http://orcid.org/0000-0001-9132-7727
Jeffrey P KrischerHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.

Funding

Limited Competition: Continued Follow-up of Subjects and Initiation of a Second Case-control Cohort in The Environmental Determinants of Diabetes in The Young Study (TEDDY)U01DK128847 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI JEFFREY P KRISCHER · 2021 to 2026
$79.2M
Data Coordinating Center (DCC)for the Consortium for Identification of EnvironmenUC4DK095300 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2011 to 2011
$56.0M
NIH Prior Approval Process ProfessionalUL1TR002535 · NCATS · UNIVERSITY OF COLORADO DENVER · PI SOKOL, RONALD J. · 2018 to 2022
$51.1M
Data Coordinating CenterU01DK063790 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2002 to 2006
$50.1M
Follow-up on Subjects, Integrative Data Analysis and Measurement of Viral Antibodies in The Environmental Determinants of Diabetes in The Young Study (TEDDY)U01DK124166 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2020 to 2022
$31.1M
Colorado Clinical and Translational Sciences Institute (CCTSI)UM1TR004399 · NCATS · UNIVERSITY OF COLORADO DENVER · PI JANINE A HIGGINS, RONALD J. SOKOL · 2023 to 2026
$30.7M
NIDDK Follow-up on Subjects and Immunological Assessments in The Environmental Determinants of Diabetes In The Young Study (TEDDY) (UC4)UC4DK117483 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2017 to 2020
$30.4M
Establish a Data Coordinating Center (DCC) for the Consortium for Identification UC4DK100238 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2014 to 2014
$28.9M
The Study of Epigenetics and Infections in The Environmental Determinants of Diabetes in the Young (TEDDY)UC4DK112243 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2016 to 2016
$27.0M
Data Coordinating Center (DCC) for the Consortium for Identification of Environmental Determinants of Diabetes in the Young (TEDDY) StudyUC4DK106955 · NIDDK · UNIVERSITY OF SOUTH FLORIDA · PI KRISCHER, JEFFREY P · 2015 to 2015
$20.0M
The Environmental Determinants of Diabetes in Young (TEDDY0U01DK063863 · NIDDK · HOSPITAL DISTRICT OF SOUTHWEST FINLAND · PI TOPPARI, JORMA · 2003 to 2022
$13.5M
The Environmental Triggers of Diabetes (TEDDY) in SwedenU01DK063861 · NIDDK · UNIVERSITY OF WASHINGTON · PI LERNMARK, AKE · 2003 to 2022
$13.4M
NCATS NIH HHS UL1 TR000064NCATS NIH HHS UL1 TR002535NCATS NIH HHS UM1 TR004399NIDDK NIH HHS U01 DK063790NIDDK NIH HHS U01 DK063821NIDDK NIH HHS U01 DK063829NIDDK NIH HHS U01 DK063836NIDDK NIH HHS U01 DK063861NIDDK NIH HHS U01 DK063863NIDDK NIH HHS U01 DK063865NIDDK NIH HHS U01 DK124166NIDDK NIH HHS U01 DK128847NIDDK NIH HHS UC4 DK063821NIDDK NIH HHS UC4 DK063829NIDDK NIH HHS UC4 DK063836NIDDK NIH HHS UC4 DK063861NIDDK NIH HHS UC4 DK063863NIDDK NIH HHS UC4 DK063865NIDDK NIH HHS UC4 DK095300NIDDK NIH HHS UC4 DK100238NIDDK NIH HHS UC4 DK106955NIDDK NIH HHS UC4 DK112243NIDDK NIH HHS UC4 DK117483NLM NIH HHS HHSN267200700014C
6 · The paper itself

Abstract

backgroundOutliers can influence regression model parameters and change the direction of the estimated effect, over-estimating or under-estimating the strength of the association between a response variable and an exposure of interest. Identifying visit-level outliers from longitudinal data with continuous time-dependent covariates is important when the distribution of such variable is highly skewed.

objectivesThe primary objective was to identify potential outliers at follow-up visits using interquartile range (IQR) statistic and assess their influence on estimated Cox regression parameters.

methodsStudy was motivated by a large TEDDY dietary longitudinal and time-to-event data with a continuous time-varying vitamin B

resultsExtreme vitamin B

conclusionAt the exploratory data analysis stage, the IQR algorithm can be used as a data quality control tool to identify potential outliers at the visit level, which can be further investigated.

Indexed as

Data AccuracyDietHumansVitaminsVitamins

Identifiers

PMID38172348
PMCPMC11003829

What OpenQuestion holds

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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.