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The correlation between variables was explored using Spearman's rank correlation test, and for temporal changes, repeated-measures analysis of variance (ANOVA) was applied.
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Potential interactions between variables were explored as required.
Correlations between variables were explored using Pearson's coefficient.
23 Spearman correlation coefficients were calculated for continuous baseline characteristics, and associations between variables were explored.
Relationships between variables were explored using Spearman's Rank order correlations.
Phenotypic relationships between variables were explored using the survey models of Stata 10 (StataCorp 2007).
Interactions between variables were explored by considering further models including an interaction between each pair of variables with a likelihood ratio test performed by comparing the model with and without interactions to determine the significance of each interaction term.
Relationships between variables were explored using Pearson's correlation (two tail) for normally distributed data (use of CM) and Spearman's Rho correlations or Gamma co-efficient for non-parametric measurements (type of CM practitioner).
The relationship between cost and explanatory variables was explored in a regression model.
The relationship between iron status and these variables was explored.
First, the difference in estimated associations between campylobacteriosis incidence and exposure variables was explored, since different results can lead to a different understanding of the environmental factors influencing the spatial distribution of the disease.
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