Evidence map›Paper›PMID 32301414›Full record

ArticleInternational psychogeriatrics2021

Predictors of attrition in a longitudinal population-based study of aging.

Erin Jacobsen, Xinhui Ran, Anran Liu, Chung-Chou H Chang, Mary Ganguli

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In one paragraph

Article in International psychogeriatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
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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

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

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Erin JacobsenDepartment of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Xinhui RanDepartment of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
Anran LiuDepartment of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
Chung-Chou H ChangDepartment of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
Mary GanguliDepartment of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.ORCID 0000-0003-2762-7105

Funding

Mild Cognitive Impairment: A Prospective Community StudyR01AG023651 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI GANGULI, MARY · 2005 to 2020
$27.1M
Mild Cognitive Impairment: A Prospective Community StudyR37AG023651 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Carmen Andreescu, MARY GANGULI · 2021 to 2026
$18.2M
NIA NIH HHS R01 AG023651NIA NIH HHS R37 AG023651
6 · The paper itself

Abstract

backgroundLongitudinal studies predictably experience non-random attrition over time. Among older adults, risk factors for attrition may be similar to risk factors for outcomes such as cognitive decline and dementia, potentially biasing study results.

objectiveTo characterize participants lost to follow-up which can be useful in the study design and interpretation of results.

methodsIn a longitudinal aging population study with 10 years of annual follow-up, we characterized the attrited participants (77%) compared to those who remained in the study. We used multivariable logistic regression models to identify attrition predictors. We then implemented four machine learning approaches to predict attrition status from one wave to the next and compared the results of all five approaches.

resultsMultivariable logistic regression identified those more likely to drop out as older, male, not living with another study participant, having lower cognitive test scores and higher clinical dementia ratings, lower functional ability, fewer subjective memory complaints, no physical activity, reported hobbies, or engagement in social activities, worse self-rated health, and leaving the house less often. The four machine learning approaches using areas under the receiver operating characteristic curves produced similar discrimination results to the multivariable logistic regression model.

conclusionsAttrition was most likely to occur in participants who were older, male, inactive, socially isolated, and cognitively impaired. Ignoring attrition would bias study results especially when the missing data might be related to the outcome (e.g. cognitive impairment or dementia). We discuss possible solutions including oversampling and other statistical modeling approaches.

Indexed as

Activities of Daily LivingHealth BehaviorLost to Follow-UpQuality of LifeAgedAged, 80 and overAgingFemaleHumansLogistic ModelsLongitudinal StudiesMachine LearningMalePatient DropoutsPopulation Surveillanceartificial neural network (ANN)epidemiologygradient boosting machine (GBM)least absolute shrinkage and selection operator-type regression (LASSO)loss to follow-uprandom forest (RF)

Identifiers

PMID32301414
PMCPMC7572515

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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.