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BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2017

Open Access 01-12-2017 | Research article

Longitudinal drop-out and weighting against its bias

Authors: Steffen C. E. Schmidt, Alexander Woll

Published in: BMC Medical Research Methodology | Issue 1/2017

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Abstract

Background

The bias caused by drop-out is an important factor in large population-based epidemiological studies. Many studies account for it by weighting their longitudinal data, but to date there is no detailed final approach for how to conduct these weights.

Methods

In this study we describe the observed longitudinal bias and a three-step longitudinal weighting approach used for the longitudinal data in the MoMo baseline (N = 4528, 4–17 years) and wave 1 study with 2807 (62%) participants between 2003 and 2012.

Results

The most meaningful drop-out predictors were socioeconomic status of the household, socioeconomic characteristics of the mother and daily TV usage. Weighting reduced the bias between the longitudinal participants and the baseline sample, and also increased variance by 5% to 35% with a final weighting efficiency of 41.67%.

Conclusions

We conclude that a weighting procedure is important to reduce longitudinal bias in health-oriented epidemiological studies and suggest identifying the most influencing variables in the first step, then use logistic regression modeling to calculate the inverse of the probability of participation in the second step, and finally trim and standardize the weights in the third step.
Footnotes
1
Representative for German children and adolescents in the year of 2004 with respect to sex, age, region, migration status and education
 
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Metadata
Title
Longitudinal drop-out and weighting against its bias
Authors
Steffen C. E. Schmidt
Alexander Woll
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2017
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-017-0446-x

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