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We use survey data and employ negative binomial regression models.
In the empirical analysis, we use panel data and negative binomial regression models with fixed effects.
Negative binomial regression models are then used to define the role of risk factors and the diagnosing physician type.
Prevalence ratios and 95% confidence intervals were estimated with log binomial regression models.
Various negative binomial regression models have been developed to study Lyme disease in connection to climate and/or landscape factors.
We propose a Bayesian approach for the mixed-effects analysis of accuracy studies using mixed binomial regression models.
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We then examined the effect of helmet use on injury using generalized log-binomial regression models.
1: Log-binomial regression models.
It was used into the following log-binomial regression models.
Log-binomial regression models were used to calculate prevalence ratios with 95% confidence intervals.
Logarithmic-binomial regression models were used to determine relative risks (RRs) and 95% CIs for SGA.
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