G is discrete and has a range (0,1,2...20). M is continuous and varies from 0 to 1.

The linear correlation coef between M and G is about 0.6-0.7 (Pearson) for most studies, when scores are compared for every animal in the study.

In most studies, I have a control group and treatment groups, each with 5-8 animals. Traditionally, we used G to score each of the animals, and used the Dunnett's test to identify treatment groups that were significantly reduce disease severity score (just look at p values < 0.05). When I used M instead, the Dunnett's test also identifies mostly the same treatment groups as significant, but generally with higher P values. In a dose study, where multiple treatment groups were treated with titrations of a drug, G identified a few more treatment groups as significant (p<0.05) than M, which missed the very low titrations.

Question:

- Ultimately, I want to know if M is good "enough" for our needs, because it is so much faster and easier.

- What is the best way to formally measure the "sensitivity" of M compared to G?

- In practice, M identifies similar groups as significant using the Dunnett's test as G. Should I use P values from Dunnett's, or look at confidence intervals to make this point? I am concerned because P values don't address effect size, but in reality, a treatment that decreases disease score by less than 20% is not biologically interesting. In fact, M does very well for treatments that have more than 20% effect, compared to G. How do I combine effect size with confidence intervals or p values, to assess whether M can replace G in some circumstances?

I would really appreciate any help!!