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Statistical analyses were performed by using the R statistical software package release 1.3.0 (http://www.r-project.org), with the methods outlined in Venables and Ripley (26) for generalized linear models of binomial data.

As the PTSD symptom score is a count variable and as our data did not fulfill the assumptions to run a linear regression, we applied an extended generalized linear model for count data based on the assumption of a negative binomial distribution of the data.

*Estimated by a generalized linear model for the binomial family with a log link.

The effect on EPTR and ROTR at each intervention group was modeled using generalized linear models (GLM) for binomial data (link function identity), clustered by site.

The use of generalized linear models (GLM) for binomial data allowed us to calculate the impact of the interventions considering these pre and post measures in each reporting unit maintaining a clustered analysis.

A generalized linear model for these three binomial responses was fitted, by using a log link function for the linear predictor to obtain regression coefficients that represented the log-risk ratio of the outcome.

For each outcome, we estimated adjusted differences between groups using a generalized linear model; for binary outcomes, we fit linear binomial models.

GEE is an extension of generalized linear models for nonindependent data (Zeger and Liang 1986).

Comparisons of this primary outcome will be based on generalized linear models for longitudinal data.

Prevalence ratios and 95% CIs were calculated to measure the degree of association between independent variables and MDR TB through generalized linear models for the binomial family.

Multilevel data analysis examples using R. Topics include: two-level nested data, growth curve modeling, generalized linear models for counts and categorical data, nonlinear models, three-level analyses.