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Using the nationally representative population-based Rwanda Demographic and Health Surveys 2005 and 2010, we conducted a statistical analysis using multilevel logistic random-effects models.
In light of this, we can interpret narrow-sense heritability in terms of the ratio of predictable to total variation in our logistic random-effects model.
For individual-level analysis, we used a multilevel logistic random-effects model which allowed us to control for clustering effects at the PSU and district levels.
The analyses were performed using linear and logistic random-effects models, clustering on 139 small areas considered homogenous with respect to socioeconomic characteristics.
A simulation study by Austin [ 34] suggested that the statistical power of GEE is the highest among t-test, Wilcoxon rank sum test, permutation test, adjusted chi-square test and logistic random-effects model for the analysis of CRTs.
The logistic random-effects model for BP control estimated an increase of 4.2% (2.2%–6.1%; p<0.01) in the odds of having a controlled BP, per month of enrollment.
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We fitted a logistic random effects regression model with only a constant to obtain the estimate and the uncertainty surrounding it.
We compared ten different implementations of logistic random effects models.
All packages are able to deal with the binary logistic random effects model.
For both cases only the binary logistic random effects model (1.1) was fitted to the data.
At this moment, we cannot recommend this SAS procedure for fitting logistic random effects regression models.
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