Hostname: page-component-f554764f5-68cz6 Total loading time: 0 Render date: 2025-04-10T13:32:28.003Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected].
Get access

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Agogo, G. O., Ramsey, C. M., Gnjidic, D., Moga, D. C. and Allore, H. (2018). Longitudinal associations between different dementia diagnoses and medication use jointly accounting for dropout. International Psychogeriatrics, 30, 1477–1487. doi: 10.1017/S1041610218000017.CrossRefGoogle ScholarPubMed
Amalakuhan, B., et al. (2012). A prediction model for COPD readmissions: catching up, catching our breath, and improving a national problem. Journal of Community Hospital Internal Medicine Perspectives, 2. doi: 10.3402/jchimp.v2i1.9915.CrossRefGoogle ScholarPubMed
Babatunde, O. A., et al. (2017). Predictors of retention among African Americans in a randomized controlled trial to test the Healthy Eating and Active Living in the Spirit (HEALS) intervention. Ethnicity & Disease, 27, 265–272. doi: 10.18865/ed.27.3.265.CrossRefGoogle Scholar
Bambs, C. E., et al. (2013). Sociodemographic, clinical, and psychological factors associated with attrition in a prospective study of cardiovascular prevention: the heart strategies concentrating on risk evaluation study. Annals of Epidemiology, 23, 328–333. doi: 10.1016/j.annepidem.2013.02.007.CrossRefGoogle Scholar
Burke, S. L., et al. (2019). Factors influencing attrition in 35 Alzheimerʼs Disease Centers across the USA: a longitudinal examination of the National Alzheimerʼs Coordinating Centerʼs Uniform Data Set. Aging Clinical and Experimental Research, 31, 1283–1297. doi: 10.1007/s40520-018-1087-6.CrossRefGoogle ScholarPubMed
Cacioppo, J. T. and Cacioppo, S. (2018). The population-based longitudinal Chicago Health, Aging, and Social Relations Study (CHASRS): study description and predictors of attrition in older adults. Archives of Scientific Psychology, 6, 21–31. 10.1037/arc0000036.CrossRefGoogle ScholarPubMed
Chang, C. C., Yang, H. C., Tang, G. and Ganguli, M. (2009). Minimizing attrition bias: a longitudinal study of depressive symptoms in an elderly cohort. International Psychogeriatrics, 21, 869–878. doi: 10.1017/S104161020900876X.CrossRefGoogle Scholar
Chatfield, M. D., Brayne, C. E. and Matthews, F. E. (2005). A systematic literature review of attrition between waves in longitudinal studies in the elderly shows a consistent pattern of dropout between differing studies. Journal of Clinical Epidemiology, 58, 13–19. doi: 10.1016/j.jclinepi.2004.05.006.CrossRefGoogle Scholar
Chu, A., et al. (2008). A decision support system to facilitate management of patients with acute gastrointestinal bleeding. Artificial Intelligence in Medicine, 42, 247–259. doi: 10.1016/j.artmed.2007.10.003.CrossRefGoogle ScholarPubMed
Daza, E. J., Hudgens, M. G. and Herring, A. H. (2017). Estimating inverse-probability weights for longitudinal data with dropout or truncation: the xtrccipw command. The Stata Journal, 17, 253–278.CrossRefGoogle ScholarPubMed
Deeg, D. J., van Tilburg, T., Smit, J. H. and de Leeuw, E. D. (2002). Attrition in the longitudinal aging study Amsterdam. The effect of differential inclusion in side studies. Journal of Clinical Epidemiology, 55, 319–328. doi: 10.1016/s0895-4356(01)00475-9.CrossRefGoogle ScholarPubMed
Dorsett, R. (2010). Adjusting for nonignorable sample attrition using survey substitutes identified by propensity score matching: an empirical investigation using labour market data. Journal of Official Statistics, 26, 105–125.Google Scholar
Fillenbaum, G. G. (1988). Multidimensional Functional Assessment of Older Adults: The Duke Older Americans Resources and Services Procedures. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.Google Scholar
Folstein, M. F., Folstein, S. E. and McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198. doi: 10.1016/0022-3956(75)90026-6.CrossRefGoogle ScholarPubMed
Gallagher, D., Fischer, C. E. and Iaboni, A. (2017). Neuropsychiatric symptoms in mild cognitive impairment. The Canadian Journal of Psychiatry, 62, 161–169. doi: 10.1177/0706743716648296.CrossRefGoogle ScholarPubMed
Ganguli, M., et al. (2020). Aging, diabetes, obesity, and cognitive decline: A population-based study. Journal of the American Geriatrics Society, published online ahead of print, 2020 Feb 4. doi: 10.1111/jgs.16321.CrossRefGoogle ScholarPubMed
Ganguli, M., Fu, B., Snitz, B. E., Hughes, T. F. and Chang, C. C. (2013). Mild cognitive impairment: incidence and vascular risk factors in a population-based cohort. Neurology, 80, 2112–2120. doi: 10.1212/WNL.0b013e318295d776.CrossRefGoogle Scholar
Ganguli, M., Gilby, J., Seaberg, E. and Belle, S. (1995). Depressive symptoms and associated factors in a rural elderly population: the MoVIES project. The American Journal of Geriatric Psychiatry, 3, 144–160. doi: 10.1097/00019442-199500320-00006.CrossRefGoogle Scholar
Ganguli, M., et al. (2015). Who wants a free brain scan? Assessing and correcting for recruitment biases in a population-based sMRI pilot study. Brain Imaging and Behavior, 9, 204–212. doi: 10.1007/s11682-014-9297-9.CrossRefGoogle Scholar
Ganguli, M., Snitz, B., Vander Bilt, J. and Chang, C. C. (2009). How much do depressive symptoms affect cognition at the population level? The Monongahela-Youghiogheny Healthy Aging Team (MYHAT) study. International Journal of Geriatric, 24, 1277–1284. doi: 10.1002/gps.2257.Google Scholar
Glymour, M. M., Chene, G., Tzourio, C. and Dufouil, C. (2012). Brain MRI markers and dropout in a longitudinal study of cognitive aging: the Three-City Dijon Study. Neurology, 79, 1340–1348. doi: 10.1212/WNL.0b013e31826cd62a.CrossRefGoogle Scholar
Hara, M., et al. (2015). Factors associated with non-participation in a face-to-face second survey conducted 5 years after the baseline survey. Journal of Epidemiology, 25, 117–125. doi: 10.2188/jea.JE20140116.CrossRefGoogle Scholar
Hastie, T., Tibshirani, R. and Friedman, J. (2008). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer.Google Scholar
Henderson, R., Diggle, P. and Dobson, A. (2000). Joint modelling of longitudinal measurements and event time data. Biostatistics, 1, 465–480.CrossRefGoogle ScholarPubMed
Hsich, E., Gorodeski, E. Z., Blackstone, E. H., Ishwaran, H. and Lauer, M. S. (2011). Identifying important risk factors for survival in patient with systolic heart failure using random survival forests. Circulation: Cardiovascular Quality and Outcomes, 4, 39–45. doi: 10.1161/CIRCOUTCOMES.110.939371.Google ScholarPubMed
Kuh, D., et al. (2016). The MRC National Survey of Health and Development reaches age 70: maintaining participation at older ages in a birth cohort study. European Journal of Epidemiology, 31, 1135–1147. doi: 10.1007/s10654-016-0217-8.CrossRefGoogle ScholarPubMed
Li, Q. and Su, L. (2018). Accommodating informative dropout and death: a joint modelling approach for longitudinal and semi-competing risks data. Journal of the Royal Statistical Society: Series C Applied Statistics, 67, 145–163. doi: 10.1111/rssc.12210.Google ScholarPubMed
Liu, S. Y., Manly, J. J., Capistrant, B. D. and Glymour, M. M. (2015). Historical differences in school term length and measured blood pressure: contributions to persistent racial disparities among US-born adults. PLoS One, 10, e0129673. doi: 10.1371/journal.pone.0129673.CrossRefGoogle ScholarPubMed
Lo-Ciganic, W. H., et al. (2019). Evaluation of machine-learning algorithms for predicting opioid overdose risk among Medicare beneficiaries with opioid prescriptions. JAMA Network Open, 2, e190968. doi: 10.1001/jamanetworkopen.2019.0968.CrossRefGoogle ScholarPubMed
Matthews, F. E., Chatfield, M., Brayne, C. and Medical Research Council Cognitive Function and Ageing Study (MRC CFAS) (2006). An investigation of whether factors associated with short-term attrition change or persist over ten years: data from the Medical Research Council Cognitive Function and Ageing Study (MRC CFAS). BMC Public Health, 6, 185. doi: 10.1186/1471-2458-6-185.CrossRefGoogle Scholar
Mein, G., et al. (2012). Predictors of two forms of attrition in a longitudinal health study involving ageing participants: an analysis based on the Whitehall II study. BMC Medical Research Methodology, 12, 164. doi: 10.1186/1471-2288-12-164.CrossRefGoogle Scholar
Morris, J. C. (1993). The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 43, 2412–2414. doi: 10.1212/wnl.43.11.2412-a.CrossRefGoogle ScholarPubMed
Mungas, D., Marshall, S. C., Weldon, M., Haan, M. and Reed, B. R. (1996). Age and education correction of Mini-Mental State Examination for English and Spanish-speaking elderly. Neurology, 46, 700–706. doi: 10.1212/wnl.46.3.700.CrossRefGoogle ScholarPubMed
R Core Team (2014). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org.Google Scholar
Radloff, L. S. (1977). The CES-D Scale: a self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401.CrossRefGoogle Scholar
Salthouse, T. A. (2014). Selectivity of attrition in longitudinal studies of cognitive functioning. The Journals of Gerontology: Series B, 69, 567–574. doi: 10.1093/geronb/gbt046.CrossRefGoogle ScholarPubMed
Snitz, B. E., et al. (2012). Subjective cognitive complaints of older adults at the population level: an item response theory analysis. Alzheimer Disease & Associated Disorders, 26, 344–351. doi: 10.1097/WAD.0b013e3182420bdf.CrossRefGoogle Scholar
Steptoe, A., Breeze, E., Banks, J. and Nazroo, J. (2013). Cohort profile: the English longitudinal study of ageing. International Journal of Epidemiology, 42, 1640–1648. doi: 10.1093/ije/dys168.CrossRefGoogle ScholarPubMed
Thottakkara, P., et al. (2016). Application of machine learning techniques to high-dimensional clinical data to forecast postoperative complications. PLoS One, 11, e0155705. doi: 10.1371/journal.pone.0155705.CrossRefGoogle ScholarPubMed
Van Beijsterveldt, C. E., et al. (2002). Predictors of attrition in a longitudinal cognitive aging study: the Maastricht Aging Study (MAAS). Journal of Clinical Epidemiology, 55, 216–223. doi: 10.1016/s0895-4356(01)00473-5.CrossRefGoogle Scholar
Weuve, J., et al. (2015). Guidelines for reporting methodological challenges and evaluating potential bias in dementia research. Alzheimer’s & Dementia, 11, 1098–1109. doi: 10.1016/j.jalz.2015.06.1885.CrossRefGoogle ScholarPubMed
Wolinsky, F. D., et al. (2010). Speed of processing training protects self-rated health in older adults: enduring effects observed in the multi-site ACTIVE randomized controlled trial. International Psychogeriatrics, 22, 470–478. 10.1017/S1041610209991281.CrossRefGoogle ScholarPubMed
Young, A. F., Powers, J. R. and Bell, S. L. (2006). Attrition in longitudinal studies: who do you lose? Australian and New Zealand Journal of Public Health, 30, 353–361. doi: 10.1111/j.1467-842x.2006.tb00849.x.CrossRefGoogle Scholar
Supplementary material: File

Jacobsen et al. Supplementary Materials

Jacobsen et al. Supplementary Materials

Download Jacobsen et al. Supplementary Materials(File)
File 26.7 KB