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Non-linear mixed models in the analysis of mediated longitudinal data with binary outcomes

Overview of attention for article published in BMC Medical Research Methodology, January 2012
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Title
Non-linear mixed models in the analysis of mediated longitudinal data with binary outcomes
Published in
BMC Medical Research Methodology, January 2012
DOI 10.1186/1471-2288-12-5
Pubmed ID
Authors

Emily A Blood, Debbie M Cheng

Abstract

Structural equation models (SEMs) provide a general framework for analyzing mediated longitudinal data. However when interest is in the total effect (i.e. direct plus indirect) of a predictor on the binary outcome, alternative statistical techniques such as non-linear mixed models (NLMM) may be preferable, particularly if specific causal pathways are not hypothesized or specialized SEM software is not readily available. The purpose of this paper is to evaluate the performance of the NLMM in a setting where the SEM is presumed optimal.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 6%
Spain 1 3%
United States 1 3%
Sweden 1 3%
Unknown 30 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 20%
Student > Ph. D. Student 6 17%
Professor 4 11%
Student > Master 4 11%
Student > Bachelor 2 6%
Other 6 17%
Unknown 6 17%
Readers by discipline Count As %
Mathematics 3 9%
Psychology 3 9%
Medicine and Dentistry 3 9%
Nursing and Health Professions 3 9%
Economics, Econometrics and Finance 3 9%
Other 11 31%
Unknown 9 26%