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This possibility must be examined by iterating the negative binomial regression analyses to account for all possible combinations.
Univariate and multivariate log binomial regression analyses to identify factors associated with willingness to accept free HIV testing are shown in Table 3.
Since the dependent variables used in our analyses are count measures and therefore represent a Poisson distribution, we employed negative binomial regression analyses to examine the independent and interactive effects of DRD2 and DRD4 on conduct disorder and antisocial behavior.
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Negative binomial regression analyses were used to determine the percentage change in rate [ 21].
Multilevel log binomial regression analyses were performed to assess the effect of BMI on the risk of SSIs while accounting for clustering within hospitals.
We used Intercooled STATA 9.0 (Stata Corporation, College Station, Texas) for log binomial regression analyses in order to present risk parameters as relative risks.
Published binomial regression analyses from the treat-to-target studies suggest that this effect on HbA1c might be quite large and thus clinically relevant 5, 8.
Multiple and negative binomial regression analyses, where appropriate, were used to test the effect of treatment on all primary and secondary endpoints.
Univariate and multivariate logistic regression (adjusted odds ratios) and negative binomial regression analyses (incidence rate ratios) were used to identify significant independent associations between risk factors with caries experience and dmft counts.
Negative binomial regression analyses with a log-link function were used to determine the relationship between having multiple diseases and the number of contacts with general practice.
In order to analyze these rate data, negative binomial regression analyses were conducted (Agresti 2002). 2 Results were expressed as odds ratios.
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