I'm writing a medical paper on asthma and I'm afraid I 've reached a point where I am unconfident of my statistics knowledge.

I am hypothesizing that there is a relationship between elevated concentrations of a substance in the blood (we will call it X) and severity of asthma.

Several papers have grouped patients by severity: [mild], [moderate] or [severe]

And for each group they have provided n (number of patients) and mean (with S.D) concentration of X.

Now I could use standard one-way ANOVA and show that the means of X for each group are significantly "different". However, it is my hypothesis (and is the case from the data) that in fact X rises in accordance with asthma severity (i.e. mild < moderate < severe).

All of this is fine and the ANOVA still returns significance, but ANOVA would also do so for anything where the means are not the same (i.e. significance could occur if mild > moderate > severe or mild > moderate < severe).

What I would like to know: is there a more elegant way of testing for the "directionality" of X amongst the different groups? And specifically to only seek significance for mild < moderate < severe. In some ways it's slightly analogous to doing a one-tailed t-test, but of course this is not possible for ANOVA. Do I build up 2 t-tests (mild+moderate & moderate+severe) and then somehow combine them?

Or is what I am doing unnecessary or philosophically wrong and should I just stick with ANOVA?

All feedback very much appreciated.

Seth