Multivariate analyses were performed in STATA 9.1 by negative binomial regression models that took into account the sample scheme (survey statistics SVY: nbreg) [ 25].
Stringent criteria were set for determining DM sites in the MbMt data set vs. the other cell cultures, namely, at least a 50% difference in methylation in the myogenic cells vs. the other cell cultures at a significance level of p < 0.01, as determined by fitted binomial regression models at each monitored CpG site.
In some cases, the Poisson regression model was adjusted to account for: a) overdispersion, either by estimating an additional dispersion parameter using quasi-Poisson regression models [ 15, 30] or more formally by using negative binomial regression models [ 28], b) excess zero counts in the observations, by using Zero-inflated Poisson regression models [ 12, 16].
We analysed binary outcomes by using log binomial regression models, with treatment effects expressed as relative risks.
We checked for overdispersion by fitting negative binomial regression models and assessing the significance of the extra variation parameter.
Temporal trends were assessed by using univariate negative binomial regression models, stratified by source category.
The rates of the place and activity-specific falls and injurious falls were computed and compared by sex using negative binomial regression models that account for over dispersion.
We fitted separate negative binomial regression models by cancer type (that is, melanoma of the skin, NMSCs) for the month of birth (Table 1).
Model results are shown in Table 9.> -wrap-foot> Model 1 estimated using ordinary least squares; Model 2 estimated using negative binomial; * <0.10 **<0.05 ***<0.01 The relationships between functional limitations and indirect costs were determined by least squares and negative binomial regression models.
To explore whether the number of countries and number of months in which we observe search volume above the threshold is influenced by gender, we fit two negative binomial regression models that both use gender as the independent variable.
