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Underlying mechanisms are explored with logistic (interaction) regression analysis.
Factors of patients, professional practice, and hospital setting were explored with logistic regression modeling.
For the descriptive categorical data the comparisons were explored with logistic regression analyses.
Associations were explored with logistic regression modelling controlling sequentially for potentially confounding factors; age, gender, ethnicity and lone parenthood.
Predictor factors for each suicidal behaviour were explored with logistic regression analysis [ 23] and the results are presented as odds ratios (OR's) with 95% confidence intervals (CI's), and all the CI's are adjusted for design effects.
Associations between socio-demographic, anthropometric and clinical factors and the prevalence of hypertension were explored with logistic regression analysis, the diagnosis of hypertension being entered as a dependent dichotomic variable.
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This association was further explored with binary logistic regression, and odds ratios were generated.
The relationship between the various predictors and probability of having ASB were explored with a series of logistic regression models.
The relationship between the various factors (age, gender, highest educational level achieved, group [SCD vs. AA], employment status) and depression was explored with a series of logistic regression models, and their relationship with loneliness was explored with multivariate linear regression.
We explored with the use of a logistic regression the factors that could influence the risk of households to need hardship financing when paying for healthcare costs (Table 3).
Final harvest data were explored with linear and non-linear regression including logistic sigmoid and logistic with hormesis approaches (Stephenson et al. 2000), with the models selected being those with lowest variance of residuals.
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