PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of bmcphBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Public Health
 
BMC Public Health. 2012; 12: 918.
Published online Oct 29, 2012. doi:  10.1186/1471-2458-12-918
PMCID: PMC3503744
Attrition and generalizability in longitudinal studies: findings from a 15-year population-based study and a Monte Carlo simulation study
Kristin Gustavson,corresponding author1 Tilmann von Soest,1,2 Evalill Karevold,1 and Espen Røysamb1,2
1Norwegian Institute of Public Health, Division of Mental Health, Department of Child and Adolescent Mental Health, P.O. Box 4404, Nydalen, NO-0403, Oslo, Norway
2Department of Psychology, University of Oslo, P.O. Box 1072, Blindern, NO-0316, Oslo, Norway
corresponding authorCorresponding author.
Kristin Gustavson: KristinBrun.Gustavson/at/fhi.no; Tilmann von Soest: t.v.soest/at/psykologi.uio.no; Evalill Karevold: Evalill.Karevold/at/fhi.no; Espen Røysamb: espen.roysamb/at/psykologi.uio.no
Received April 4, 2012; Accepted October 17, 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
Articles from BMC Public Health are provided here courtesy of
BioMed Central