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Adjusted risk ratios were obtained by binomial regression with robust standard errors.
Prevalence ratios and 95% confidence intervals were estimated by binomial regression with the log link function [ 19].
Prevalence ratios (PRs) and 95% confidence intervals (CIs) were estimated by binomial regression with the log link function [ 9- 11].
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This pattern was confirmed by binomial regression models (table 1) with decreases in the between-PCT SD with increasing gestation.
Data concerning adherence will be analysed by multivariate binomial regression with a latent variable with a level for each practice.
The probability for TV > 6 ml/kg ideal body weight (IBW) was assessed by binomial regression and expressed as odds ratio (OR) with 95% confidence intervals (CI).
Predictors of infection were examined by Cox regression and disease risk by binomial regression.
For the practice pattern outcomes, we modeled the annual cesarean rate by using negative binomial regression with delivery volume as the exposure.
Due to the observed seasonal variation, we smoothed the trend by using a negative binomial regression with quadratic time trend.
Table 4 Negative binomial regression with startups as the dependent variable Estimate Std.
We used linear binomial regression, with frequent use of organic food (sum index >6) as the binomial outcome variable.
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