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The effect of baseline differences in catastrophising was evaluated by entering this variable into the multilevel analysis models.
In both analyses, we controlled for sex by entering this variable into the equation on step 1.
The potential moderating role of baseline level of depression (met/did not meet MDE cutpoint on MFQ) was assessed by entering this variable into the ANCOVA as an interaction with treatment allocation.
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Tests for trends were performed by treating each categorized variable as a continuous term and by entering the variable into the fully adjusted linear regression model.
A final model was achieved by entering the variables retained in the backward selection model.
The multivariate analyses were performed by entering the variables that had a p-value of < 0.1 with subsequent removal of the least significant variables in a stepwise fashion.
By entering these variables first, their effects were controlled in subsequent steps investigating the independent effects of cognitive variables on outcomes.
In addition, possible confounding effects of age and sex (i.e. biological factors) were controlled by entering these variables into the model.
But only education status remained statistically significant (P < 0.05) after controlling confounding effect with multivariate regression by entering those variables that have a P value of ≤0.2.
The multiple model was built by entering those variables that had univariate statistical significance with a p < 0.05 in the correlation, retaining those variables with p < 0.05 in the final regression model.
To address the first hypothesis of the study, a separate model B was fitted for each benefits data variable and the Townsend score in turn by entering these variables one by one to the compositional model A. Each variable was modelled as a z-score, obtained by subtracting the mean and dividing by the standard deviation of the distribution of scores (Table 1).
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