To choose a dose-response model in Prism, you need to answer four questions:
Are the X values concentrations (or doses) or the logarithm of concentration or dose?
Prism 7 offers two sets of equations for dose-response curves. One set assumes X is the logarithm of dose or concentration. The other set assumes X is the dose or concentration. Be sure to pick an equation that matches the data.
Stimulation or inhibition?
Prism offers one set of dose-response equations for stimulation and another set for inhibition. The inhibitory equations are set up to run downhill. The only difference is that the inhibitory equations fit the IC50 ("I" for inhibition) while the stimulation equations fit the EC50 ("E" for effective).
If the curve goes up hill, choose from the set of stimulation equations. If the curve goes down hill, choose from the set of inhibition equations.
Standard slope or variable slope?
Prism offers equations using a standard slope, which have a Hill slope of 1.00 (for stimulation) or -1.00 (for inhibition), and variable slope (fit by Prism). . The equations that don't have 'variable slope' in their name assume the standard slope. The standard slope is expected when measuring binding of a ligand to a receptor where there is no heterogeneity or cooperativity. But it turns out that many other log(dose) vs. response curves have the same standard slope.
If you have lots of data points (more than a dozen, perhaps lots more), then you can fit the slope by picking a variable slope equation. If you have fewer data points, and a standard system, it makes sense to choose an equation with a standard slope.
There are many situations where it is not clear which approach is better.
Normalized or not?
If your data have been normalized so the curve runs from Y= 0 to Y=100, you may wish to choose a normalized model. These models don't fit the bottom and top plateaus, but rather force the bottom plateau to equal 0 and the top plateau to equal 100. Only choose a 'normalized response' equation when you have determined the values that define 0 and 100 very precisely. Just because the data have been normalized doesn't mean to have to constrain the curve in that way.
There are many situations where it is not clear whether it makes sense to use a normalized model or not. It is not always a straightforward decision.
Special dose-response models
Prism has a set of special models used for special dose-response situations:
