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Each model for a domain of HRQoL included all demographic and clinical variables and the model coefficients are shown by variable in figures 1 and 2. Demographic and clinical variables which were significant at the 10% level were extracted and are presented in table 3 by HRQoL domain.
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No other variable in Figure 2 demonstrated a statistically significant association with stocking when controlling for these variables.
These cross-level interactions are illustrated for the Incapacity Benefit and Severe Disablement Allowance variable in Figure 1.
As discussed at length in Myhrvold (2013), the choice of independent variable in Figure 3 is inconsistent with that in Figure 6.
The survey items measuring each psychosocial variable in Figure 1 were adapted from the previously described survey of psychosocial predictors of colorectal cancer screening and surveillance colonoscopy among predominately male autoworkers [ 33].
Linear regression analyses treating the patient visits as independent variables (in Figures 6 and 7) yielded essentially the same conclusion when compared to using the GEE model for repeated measures (data not shown).
The remaining variables in Figure 1 will be explained as required in the following sections.
Key to Individual Variables in Figure 5E Listed left to right.
Means of IH were graphed against selected structural and behavioural variables in figure 2 to illustrate the relationship between variables.
All auxiliary variables in Figure 1 have moderate correlations of r=.5 to X1, X2, Y and all Z. Figure 1a displays the results of the CC analysis.
We chose the order of explanatory variables in Figure 4 to illustrate what would happen if all pollution is taken away, leaving only temperature as a risk factor.
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