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

Published online by Cambridge University Press:  17 April 2020

Erin Jacobsen ,
Xinhui Ran ,
Anran Liu ,
Chung-Chou H. Chang  and
Mary Ganguli Open the ORCID record for Mary Ganguli [Opens in a new window]
Erin Jacobsen*
Affiliation:
Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
Xinhui Ran
Affiliation:
Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
Anran Liu
Affiliation:
Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
Chung-Chou H. Chang
Affiliation:
Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
Mary Ganguli
Affiliation:
Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
*
Correspondence should be addressed to: Erin Jacobsen, Department of Psychiatry, School of Medicine, University of Pittsburgh, 230 McKee Place, Pittsburgh, PA15213, USA. Phone: +412 647 6619. Fax: +412 647 6555. Email: jacobsenep@upmc.edu.
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Abstract

Background:

Longitudinal 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.

Objective:

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

Methods:

In 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.

Results:

Multivariable 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.

Conclusions:

Attrition 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.

Keywords

epidemiologyloss to follow-upleast absolute shrinkage and selection operator-type regression (LASSO)random forest (RF)gradient boosting machine (GBM)artificial neural network (ANN)
Type
Original Research Article
Information
International Psychogeriatrics , Volume 33 , Special Issue 8: Issue Theme: Longitudinal Studies of Aging , August 2021 , pp. 767 - 778
Copyright
© International Psychogeriatric Association 2020

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