Technique to compare treatments ANOVA, t test I have the next problem:
I need to compare three treatments $A, B$ and $C$ for a type of pain. Every treatment has been used in 10 different patients.
and every treatment is measured with two techniques $T_1$ and $T_2.$
Both techniques have a measure before the treatment and a measure after the treatment (a measure of pain).
$T_1$ is measured before and after, i.e $T_{1,\textrm{before}}, T_{1,\textrm{after}}.$
$T2$ is measured in four parts of the body, before and after the treatment using an special device for instance:
$$T_{2,\textrm{before}_i}, ~T_{2,\textrm{after}_i}~~i\in\{1,2,3,4\}.$$
What kind of statistical technique could I use to decide which treatment $A,B$ or $C$ performs better?
I have been reading about ANOVA but I am not sure about how to use technique 2, because it has measures in four parts of the body.
Moreover, I read that ANOVA only helps to see if the means are equal or different but not to see which one is better.
I hope an explanation or any good reference to read.
 A: A few ideas come to mind. For all of them, I would create change scores from the pre and post measures and then perform all analysis on these. 
You could try multivariate ANOVA, aka MANOVA, which is like an ANOVA with multiple outcomes. You could test whether the means of the measures are jointly different across the treatment conditions. 
You could try a simple structural equation model (SEM) with a latent variable that is measured by your measures. In a latent variable model, you assume there is a latent (unobserved) variable that is measured (imperfectly) by several measures, or indicators. You can then test whether the treatment variable explains variance in the latent variable using a SEM. An issue with this method is that it requires that the indicators are uncorrelated conditional on the latent variable, which I don't know will be true in your scenario, but is testable. If this assumption is true, this would be the most sophisticated and, in my opinion, the most valid way to estimate the treatment effects of interest.
Finally, a simple method would be to create a composite score from all the methods and then just run an ANOVA on the composite score. A major problem would be justifying the method of creating the composite, but demonstrating that you get similar results regardless of how you create the composite would be an effective sensitivity analysis. This method is essentially the same as the SEM method, which can be thought of as implictly creating a composite score form the measures.
