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An independent association between baseline MFI < 2.6 and outcome was confirmed in a binary logistic analysis (odds ratio 4.594 [1.340 15.754], p = 0.015).
Variables with a P value of less than 0.2 were entered into a stepwise linear regression model or a binary logistic analysis with a forward stepwise condition.
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A regression binary logistic analysis was performed.
Because baseline score is a determinant of the magnitude of change, and there is likely to be co-dependency in the data, those factors found to be predictive of outcome, defined as variables with an unadjusted p-value of less than 0.1, were entered into a multiple regression (binary logistic) analysis, controlling for all other variables in the model (Table 6).
A normal distribution was analyzed using binary logistic analysis.
This analysis generalises a (binary) logistic regression analysis of outcome variables with more than two ordered categories.
After identifying variables associated with outcome in the univariate analysis we performed a binary logistic regression analysis to identify independent outcome predictors.
The second analysis consisted of a binary logistic regression analysis in which the effect of proximity of residential address to an infected farm on the phase I and II IgG titres 9 – 18 months after acute Q fever diagnosis was studied.
Variables that were statistically significant were selected for further analysis, in which a binary logistic regression analysis with a minimum confidence level of p < 0.05 was used to identify the most important factors affecting health service utilization by comparing health service users and non-users.
Variables that differed in the univariate analysis were inserted into a binary logistic regression analysis.
Variables that differed significantly between groups in a univariate analysis were inserted into a binary logistic regression analysis.
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