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

BMC Public Health. 2012 Oct 29;12:918. doi: 10.1186/1471-2458-12-918.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Bias*
  • Computer Simulation
  • Female
  • Follow-Up Studies
  • Humans
  • Longitudinal Studies*
  • Monte Carlo Method
  • Mothers / statistics & numerical data
  • Patient Dropouts / statistics & numerical data*
  • Risk Factors
  • Time Factors