Evidence map›Paper›PMID 27619926›Full record

ArticleEuropean journal of epidemiology2017

Multiple imputation of cognitive performance as a repeatedly measured outcome.

Andreea Monica Rawlings, Yingying Sang, Albert Richey Sharrett, Josef Coresh, Michael Griswold, Anna Maria Kucharska-Newton, Priya Palta, Lisa Miller Wruck, Alden Lawrence Gross, Jennifer Anne Deal and 2 more

Abstract read
In one paragraph

Article in European journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
6.6field-weighted citation impact, top 3% of its field
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

41 citing papers in PubMed, 46 citations in OpenAlex.

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  3. AD-related plasma biomarkers in centenarians: links to cognition and neuropathology.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
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  11. The Associations of Dietary Copper With Cognitive Outcomes.American journal of epidemiology · 2022
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  12. What difference does multiple imputation make in longitudinal modeling of EQ-5D-5L data? Empirical analyses of simulated and observed missing data patterns.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022
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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

12 authors at 4 institutions in 1 country.

Andreea Monica RawlingsDepartment of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA. arawlin2@jhu.edu.
Yingying SangDepartment of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.
Albert Richey SharrettDepartment of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.
Josef CoreshDepartment of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.
Michael GriswoldCenter of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, MS, USA.
Anna Maria Kucharska-NewtonDepartment of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
Priya PaltaDepartment of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
Lisa Miller WruckDepartment of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
Alden Lawrence GrossDepartment of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.
Jennifer Anne DealDepartment of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.
Melinda Carolyn PowerDepartment of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.
Karen Jean Bandeen-RocheDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Johns Hopkins University · USUniversity of North Carolina at Chapel Hill · USMilken Institute · USUniversity of Mississippi Medical Center · US

Funding

ARIC Neurocognitive Study (ARIC-NCS) Renewal 2023-2028U01HL096812 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI JOSEF CORESH, THOMAS H MOSLEY · 2010 to 2026
$65.7M
Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1M
'T V ASSESSMENT - MEMORY DISORDERED PATIENTS'P50AG005146 · NIA · JOHNS HOPKINS UNIVERSITY · PI ALBERT, MARILYN S. · 1985 to 2019
$37.6M
CARDIOVASCULAR EPIDEMIOLOGY INSTITUTIONAL TRAININGT32HL007024 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Elizabeth Selvin · 1985 to 2026
$17.5M
Atherosclerosis, Plaque and CVD in CommunitiesU01HL075572 · NHLBI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI BOERWINKLE, ERIC A · 2004 to 2007
$15.6M
ARIC Neurocognitive Study (ARIC-NCS) Renewal 2 of 5U01HL096917 · NHLBI · UNIVERSITY OF MISSISSIPPI MED CTR · PI MOSLEY, THOMAS H · 2010 to 2018
$11.9M
Cardiovascular Epidemiology Training GrantT32HL007055 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Wayne D. Rosamond · 1986 to 2026
$10.0M
ARIC Neurocognitive Study (ARIC-NCS) Renewal UNC 4 of 5U01HL096899 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI COUPER, DAVID J · 2010 to 2018
$7.5M
ARIC Neurocognitive Study (ARIC-NCS)U01HL096902 · NHLBI · UNIVERSITY OF MINNESOTA · PI LUTSEY, PAMELA L. · 2010 to 2018
$7.4M
ARIC Neurocognitive Study (ARIC-NCS) Renewal 4 of 5U01HL096814 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI HUGHES, TIMOTHY M., WAGENKNECHT, LYNNE E · 2010 to 2018
$6.5M
Research Training in Age-Related Cognitive DisordersT32AG027668 · NIA · JOHNS HOPKINS UNIVERSITY · PI MARILYN S. ALBERT · 2007 to 2026
$6.3M
The ARIC and Neurocognitive Longitudinal StudyR01HL070825 · NHLBI · UNIVERSITY OF MISSISSIPPI MEDICAL CENTER · PI MOSLEY, THOMAS H · 2002 to 2004
$5.1M
NCATS NIH HHS UL1 TR001079NHLBI NIH HHS HHSN268201100005CNHLBI NIH HHS HHSN268201100005GNHLBI NIH HHS HHSN268201100005INHLBI NIH HHS HHSN268201100006CNHLBI NIH HHS HHSN268201100007CNHLBI NIH HHS HHSN268201100007INHLBI NIH HHS HHSN268201100008CNHLBI NIH HHS HHSN268201100008INHLBI NIH HHS HHSN268201100009CNHLBI NIH HHS HHSN268201100009INHLBI NIH HHS HHSN268201100010CNHLBI NIH HHS HHSN268201100011CNHLBI NIH HHS HHSN268201100011INHLBI NIH HHS HHSN268201100012CNHLBI NIH HHS R01 HL070825NHLBI NIH HHS T32 HL007024NHLBI NIH HHS T32 HL007055NHLBI NIH HHS U01 HL075572NHLBI NIH HHS U01 HL096812NHLBI NIH HHS U01 HL096814NHLBI NIH HHS U01 HL096899NHLBI NIH HHS U01 HL096902NHLBI NIH HHS U01 HL096917NIA NIH HHS K99 AG052830NIA NIH HHS P50 AG005146NIA NIH HHS T32 AG027668
6 · The paper itself

Abstract

Longitudinal studies of cognitive performance are sensitive to dropout, as participants experiencing cognitive deficits are less likely to attend study visits, which may bias estimated associations between exposures of interest and cognitive decline. Multiple imputation is a powerful tool for handling missing data, however its use for missing cognitive outcome measures in longitudinal analyses remains limited. We use multiple imputation by chained equations (MICE) to impute cognitive performance scores of participants who did not attend the 2011-2013 exam of the Atherosclerosis Risk in Communities Study. We examined the validity of imputed scores using observed and simulated data under varying assumptions. We examined differences in the estimated association between diabetes at baseline and 20-year cognitive decline with and without imputed values. Lastly, we discuss how different analytic methods (mixed models and models fit using generalized estimate equations) and choice of for whom to impute result in different estimands. Validation using observed data showed MICE produced unbiased imputations. Simulations showed a substantial reduction in the bias of the 20-year association between diabetes and cognitive decline comparing MICE (3-4 % bias) to analyses of available data only (16-23 % bias) in a construct where missingness was strongly informative but realistic. Associations between diabetes and 20-year cognitive decline were substantially stronger with MICE than in available-case analyses. Our study suggests when informative data are available for non-examined participants, MICE can be an effective tool for imputing cognitive performance and improving assessment of cognitive decline, though careful thought should be given to target imputation population and analytic model chosen, as they may yield different estimands.

Indexed as

CognitionData Interpretation, StatisticalModels, StatisticalResearch DesignComputer SimulationFemaleHumansMaleMiddle AgedObservational Studies as TopicOutcome Assessment, Health CareProspective StudiesBiasCognitive functionEpidemiologic methodsMissing dataMultiple imputationProspective study

Identifiers

PMID27619926
PMCPMC5332286
OpenAlexW2519103737

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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 OpenQuestion graph.