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Predictors of attrition in a longitudinal population-based study of aging
Jacobsen E, Ran X, Liu A, Chang CH, Ganguli M.. International psychogeriatrics, 2020-04-17
Topic: Risk, prevention and care in population studies
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.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Jacobsen, E., Ran, X., Liu, A., Chang, C. H., & Ganguli, M. (2021). Predictors of attrition in a longitudinal population-based study of aging. International psychogeriatrics. https://doi.org/10.1017/s1041610220000447
Vancouver
Jacobsen E, Ran X, Liu A, Chang CH, Ganguli M. Predictors of attrition in a longitudinal population-based study of aging. International psychogeriatrics. 2021. doi:10.1017/s1041610220000447. PMID: 32301414.
BibTeX
@article{jacobsen2021-32301414,
author = {Jacobsen, E. and Ran, X. and Liu, A. and Chang, C. H. and Ganguli, M.},
title = {{Predictors of attrition in a longitudinal population-based study of aging}},
journal = {International psychogeriatrics},
year = {2021},
doi = {10.1017/s1041610220000447},
pmid = {32301414},
url = {https://doi.org/10.1017/s1041610220000447}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- 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 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.
Why it matters
In a 10-year longitudinal aging study, 77% of participants dropped out, and those more likely to drop out were older, male, had lower cognitive test scores and higher clinical dementia ratings, among other factors. The authors note that attrition risk factors may resemble risk factors for cognitive decline and dementia, potentially biasing study results, but this concerns study design and interpretation, not treating, curing or preventing Alzheimer's disease.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 52/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (International psychogeriatrics) |
| Journal's citation record | 2 / 4 | International Psychogeriatrics: journal h-index 161 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2021 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 85 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 12.1 citations a year over 7 year(s) since 2021 |
| Impact for its field and year | 4 / 6 | Cited 13.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 2 / 4 | Cited 44 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Mary Ganguli: h-index 86 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Pittsburgh: institution h-index 1,470 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32301414) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 52 / 100 | — |
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Read the original
- DOI: 10.1017/s1041610220000447
- PubMed: 32301414
- Original source →
- Free full text: International Psychogeriatrics →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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