To detect differences and similarities between the tumors, we first computed the significant paths for each patient separately.
Again, solid lines represent significant paths, with the dash-dot line from alcohol (mis)use to antisocial behaviour indicating significant paths for one gender only.
A summary of the statistically significant paths for men and women is presented in Figure 2, while Table 3 summarizes the direct and indirect effects the various proposed explanatory variables has on MS. Previous reports have addressed the effect of PTH on MS, and discussed whether the variable should be adjusted for when estimating the effect of other possible predictors of MS [ 14- 17].
If we are applying our novel algorithm, e.g. to a set of cancer expression profiles that will be compared with expression profiles from a control group, our algorithm computes and outputs the significant paths for each cancer profile (tumor, patient) separately.
As can be seen from Table III and Figure 8 (green paths), age was associated with lower HRV (negative a), and participants with low HRV (irrespective of their age) showed differential expression of the spatial patterns for all ICs of RSFA (significant path b), except for IC2 and IC10.
However, our current implementation enables the user to identify a large variety of significant paths since we are searching for paths of different lengths and since we are scanning all nodes in the graph (significant paths that end or start in one the nodes of the network).
Solid line indicates significant path; dashed line indicates insignificant path.
Different significant paths were found for gender and social deprivation: impairment did not predict participation restriction for women and those most deprived, whereas these paths were significant for men and those less deprived.
The ICF model allows for all paths between the constructs to be explored, with significant paths likely to vary for different conditions.
The ICF model allows for all causal pathways between the constructs to be explored, with significant paths likely to vary for different conditions.
Table 2 shows the path estimates and t values for the significant paths only in the model.
