Email updates

Keep up to date with the latest news and content from BMC Public Health and BioMed Central.

Open Access Highly Accessed Research article

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

Kristin Gustavson1*, Tilmann von Soest12, Evalill Karevold1 and Espen Røysamb12

Author affiliations

1 Norwegian Institute of Public Health, Division of Mental Health, Department of Child and Adolescent Mental Health, P.O. Box 4404, Nydalen, NO-0403, Oslo, Norway

2 Department of Psychology, University of Oslo, P.O. Box 1072, Blindern, NO-0316, Oslo, Norway

For all author emails, please log on.

Citation and License

BMC Public Health 2012, 12:918  doi:10.1186/1471-2458-12-918

Published: 29 October 2012

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.

Keywords:
Longitudinal studies; Public health; Attrition; Bias; Simulation