Sentence examples for binary regression models were from inspiring English sources

Binary regression models were applied for the multivariate analysis.

Thus, logistic binary regression models were employed in order to fit biological data within R 3.0.0 programming environment with the aid of epicalc package [ 35].

Binary regression models were constructed using as the three outcome variables 1) successful vs. partially successful/unsuccessful treatment, 2) unsuccessful treatment vs. successful/partially successful treatment, and 3) subsequent change in ICS device or dose and, as explanatory variables, cohort membership plus possible confounding factors.

Relative predictions from the binary regression models are centered, and, in some cases, hierarchic cluster is performed by using Gene Cluster 3.0 [ 12].

A binary regression model was employed to identify the most important factors affecting agro-pastoralists' decisions to adopt specific adaptation measures.

A binary regression model was used to estimate ORs and 95% CIs for AE incidence in subgroups and in the primary study population, with adjustment for confounding factors.

The overall fit of our binary regression model was assessed using the Hosmer and Lemeshow chi-square test of goodness of fit.

The logistic binary regression model was applied such that species presence-absence was fitted with another species presence-absence in a specific set of bromeliads or ephemeral ground pools.

For the overall association, wald chi-square tests (from Type 3 Analysis of Effects) were assessed with education, wealth, parity, and marital relationship, showing significance at p < .001 Model 1 (simple binary regression model) was assessed by each explanatory variable, and the model statistics of each model are not reported in the table.

Binary logistic regression models were used to analyse the associations between non-binary factors and the presence of a true relapse or one or more false relapses.

Binary logistic regression models were developed in an attempt to take confounding factors into account.