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Longitudinal drop-out and weighting against its bias

Overview of attention for article published in BMC Medical Research Methodology, December 2017
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Title
Longitudinal drop-out and weighting against its bias
Published in
BMC Medical Research Methodology, December 2017
DOI 10.1186/s12874-017-0446-x
Pubmed ID
Authors

Steffen C. E. Schmidt, Alexander Woll

Abstract

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. 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. 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%. 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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 25%
Student > Ph. D. Student 2 17%
Student > Doctoral Student 1 8%
Student > Master 1 8%
Lecturer > Senior Lecturer 1 8%
Other 0 0%
Unknown 4 33%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 2 17%
Medicine and Dentistry 2 17%
Social Sciences 2 17%
Sports and Recreations 1 8%
Neuroscience 1 8%
Other 0 0%
Unknown 4 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 December 2017.
All research outputs
#10,884,756
of 12,281,604 outputs
Outputs from BMC Medical Research Methodology
#983
of 1,066 outputs
Outputs of similar age
#285,778
of 344,787 outputs
Outputs of similar age from BMC Medical Research Methodology
#49
of 62 outputs
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