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Negative binomial regression quantified risk ratios (RR).
Event rate ratios were estimated using a multivariable negative binomial regression model.
In the empirical analysis, we use panel data and negative binomial regression models with fixed effects.
Prevalence ratios and 95% confidence intervals were estimated with log binomial regression models.
The dose-response relation was examined using log binomial regression or Poisson regression models.
Negative binomial regression models are then used to define the role of risk factors and the diagnosing physician type.
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Log-binomial regression was used to calculate crude and adjusted prevalence ratios.
Mean/median payments, cost rates, and adjusted rate ratios based on hours worked were calculated using negative-binomial regression.
We then examined the effect of helmet use on injury using generalized log-binomial regression models.
Results of the multivariable log-binomial regression confirmed the findings of the unadjusted associations (Table 2).
1: Log-binomial regression models.
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