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Let's use an example to illustrate an interpretation of a contribution analysis.

Given some simulation results, one may want to analyse which patient descriptors impact the most the volume of a tumor at the end of the simulation, tumorVolume.tend, for a virtual population of 100 patients.

The resulting plot is the following:

On the left side are the 10 most impactful parameters. 

The first one: logExtraLivingCellsWithAngio.tmin has the highest contribution.
Moreover, given this plot, tumorVolume.tend  and logExtraLivingCellsWithAngio.tmin are positively correlated: the subpopulation containing the 50% of highest value for logExtraLivingCellsWithAngio has a median for tumorVolume.tend 360% higher than in the overall population. This can be deduced from the yellow horizontal bar.

As per the second most impactful parameter, logKdCellsByIsHuman.tmin, the correlation is negative: the sub population containing the 50% of highest value for logKdCellsByIsHuman has a median for tumorVolume.tend 98% lower than in the overall population.

If you need more details on how to run a contribution analysis and theoretical background, I encourage you to have a look at this specific How-to