You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
Mendeley readers
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
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% |