Sentence examples for binomial regression in which from inspiring English sources

In all regression analyses (now including both the logistic and log-linked binomial regression) in which time points were pooled we applied robust (Huber-White) standard errors to account for between-individual variation.

Ninety-five perconfidencedence intervals for incidence rates were calculated using the following formulas: Lower confidence limit = incidence rate - 1.96 * episodes / person - years Upper confidence limit = incidence rate + 1.96 * episodes / person - years Rate ratios were estimated using a single multivariate negative binomial regression in which episodes from all years were included.

Rate of hypoglycaemia was estimated by a negative binomial regression model, in which the number of episodes/patient-year of exposure (events/patient-year) was adjusted for country, sex, age and HbA1c at randomization.

The rate of hypoglycaemic episodes during the exposure to trial insulin was estimated by a negative binomial regression model in which the number of episodes per patient year of exposure (episodes/patient year) was adjusted by country, sex, age and HbA1c at randomisation (21).

The rate of hypoglycemic episodes during the exposure to trial insulin was estimated by a negative binomial regression model, in which the number of episodes per patient year of exposure (events per patient year) was adjusted by country, sex, age, and A1C at randomization (17).

Log-binomial regression analyses, in which we adjusted for specimen characteristics and hormonally active medications, revealed that total maternal PCB concentration was associated with a reduction in male births (p = 0.02).

Factors with significant bivariate relative risks were then included in a multivariate negative binomial regression model, which was used to test for statistical significance of the trend in initial hospitalization statewide during 2000 2011.

These data were then used to build binomial regression models, which adjusted for differences in baseline characteristics between the studies.

This has been modelled via the negative binomial regression model which generally is the most appropriate model for counts with overdispersed values.4 From a substantive viewpoint the negative binomial distribution reflects the theoretical explanations of repeat victimisation which, as seen earlier, is extensive in both years (Hilbe 2011; Tseloni 1995; Tseloni and Pease 2010).

In contrast to Poisson regression, negative binomial regression allows overdispersion, which is common in count data.

However, this problem is overcome by estimating the negative binomial regression (NBR) model, in which a cross-section heterogeneity is naturally formulated by introducing an unobserved effect into the conditional mean (Greene 2002).