I have an experiment wherein I measured uptake of a certain molecule into cells when delivered using Carrier A vs Carrier B. In other words, for example, I delivered 1, 2, 3, 4, 8, and 10 nmoles of DrugX to 6 wells of cells per dosage, HOWEVER in 3 wells DrugX was delivered using Carrier A and in the other 3 wells DrugX was delivered using Carrier B. I would like to statistically determine whether the dose-response curve I get when I used Carrier A for delivery is any different than the dose-response curve I get when I used Carrier B for delivery.
My initial thought was to run a paired T-test, but then I realized I would have to report results at each point and I would like to report on the curve as a whole. My next thought was to use a two-factor ANOVA, however from what it looks like, when it determines whether or not there is variance due to the change in carrier it looks at the average and variance of all the responses. Another thought I had would be to calculate area under the curve, but I'm not sure how. I finally settled on running a nonlinear regression on the Carrier A data, then on the Carrier B data, then as the dataset as a whole and compared the resulting fit curves. I got a nice p-value, however the $R^2$ values are lower than I'd like (about 0.9) because the curves didn't model especially well.
What would you do? What is the industry standard?