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Attrition and generalizability in longitudinal studies: findings from a 15-year population-based study and a Monte Carlo simulation study

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Gustavson, Kristin, von Soest, Tilmann, Karevold, Evalill, Røysamb, Espen·2012·BMC Public Health
DOI 10.1186/1471-2458-12-918PMC PMC3503744
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Abstract

BACKGROUND: Attrition is one of the major methodological problems in longitudinal studies. It can deteriorate generalizability of findings if participants who stay in a study differ from those who drop out. The aim of this study was to examine the degree to which attrition leads to biased estimates of means of variables and associations between them. METHODS: Mothers of 18-month-old children were enrolled in a population-based study in 1993 (N=913) that aimed to examine development in children and their families in the general population. Fifteen years later, 56% of the sample had dropped out. The present study examined predictors of attrition as well as baseline associations between variables among those who stayed and those who dropped out of that study. A Monte Carlo simulation study was also performed. RESULTS: Those who had dropped out of the study over 15 years had lower educational level at baseline than those who stayed, but they did not differ regarding baseline psychological and relationship variables. Baseline correlations were the same among those who stayed and those who later dropped out. The simulation study showed that estimates of means became biased even at low attrition rates and only weak dependency between attrition and follow-up variables. Estimates of associations between variables became biased only when attrition was dependent on both baseline and follow-up variables. Attrition rate did not affect estimates of associations between variables. CONCLUSIONS: Long-term longitudinal studies are valuable for studying associations between risk/protective factors and health outcomes even considering substantial attrition rates.

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Figure 1 Theoretical model guiding the simulation study. Note: The association between Baseline predictor and Liability of dropping out is the sum of the direct path (b) and the indirect path via follow-up outcome (a x c). The association between Follow-up outcome and Liability of dropping out is the sum of the direct path (c) and the indirect path via Baseline predictor (a x b). This is accounted for in the analyses.
Figure 1 Theoretical model guiding the simulation study. Note: The association between Baseline predictor and Liability of dropping out is the sum of the direct path (b) and the indirect path via follow-up outcome (a x c). The association between Follow-up outcome and Liability of dropping out is the sum of the direct path (c) and the indirect path via Baseline predictor (a x b). This is accounted for in the analyses.CC BY · PubMed Central

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Research works printed but not linked: 3

  1. Schaffer RH: Social Development. 1996, Blackwell Plublishers Inc., Oxford
  2. Muthén LK, Muthén BO: Mplus user’s guide. 2010, Muthén & Muthén, Los Angeles, 6
  3. Hesbacher PT, Rickels K, Morris RJ, Newman H, Rosenfeld H: Psychiatric illness in family practice. J Clin Psychiatry. 1980, 41: 6-10.

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Metadata

Authors
Gustavson, K., von Soest, T., Karevold, E., & Røysamb, E.
Affiliations
Norwegian Institute of Public Health (NO)
Corresponding author
Gustavson, Kristin
ORCID
Venue
BMC Public Health
Publisher
Springer Science and Business Media LLC
Published
2012-10-29
Volume
12
Issue
1
Pages
918-918
ISSN
1471-2458 (ISSN-L)
DOI
10.1186/1471-2458-12-918
PMC
PMC3503744
OpenAlex
W1995178720
Cited by
679
References
45
Citations by year
2026: 68 · 2025: 84 · 2024: 79 · 2023: 61 · 2022: 75 · 2021: 55 · 2020: 55 · 2019: 36 · 2018: 46 · 2017: 38 · 2016: 20 · 2015: 30 · 2014: 15 · 2013: 11 · 2012: 4
FWCI
18.31
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
Funders
Norges Forskningsråd
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords
attrition, longitudinal study, generalizability, selection bias, dropout, educational attainment, child development, mothers, Monte Carlo simulation
MeSH terms
Adult, Computer Simulation, Female, Follow-Up Studies, Humans, Longitudinal Studies, Monte Carlo Method, Mothers, Patient Dropouts, Risk Factors, Time Factors, Bias

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