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Verlagsausgabe
DOI: 10.5445/IR/1000082982
Veröffentlicht am 22.05.2018
Originalveröffentlichung
DOI: 10.1186/s12874-017-0446-x
Web of Science
Zitationen: 2

Longitudinal drop-out and weighting against its bias

Schmidt, Steffen C. E.; Woll, Alexander

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 ... mehr


Zugehörige Institution(en) am KIT Institut für Sport und Sportwissenschaft (IfSS)
Publikationstyp Zeitschriftenaufsatz
Jahr 2017
Sprache Englisch
Identifikator ISSN: 1471-2288
URN: urn:nbn:de:swb:90-829822
KITopen-ID: 1000082982
Erschienen in BMC medical research methodology
Band 17
Heft 1
Seiten Article: 164
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 08.12.2017
Schlagworte Representativeness; Weighting; Drop-out; Logistic regression; MoMo
Nachgewiesen in Scopus
Web of Science
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