The dose-response model has four parameters: the bottom plateau, the top plateau, the EC50, and the slope factor (which is often constrained to a standard value).

The main goal of fitting the dose-response curve in many situations is to determine the best-fit value of the EC50, which is the concentration that provokes a response halfway between the top and bottom plateaus. If those plateaus are not well defined, the EC50 will be very uncertain. Think of it this way: If you have not defined "100" and "0" very precisely, you also have not defined "50" precisely, and therefore cannot determine the EC50 precisely.

One way to solve the problem is to constrain the Top or Bottom, or both, to control values. Another alternative is to normalize your data so responses run from 0 to 100, and then choose a "normalized response" 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.

Prism makes it easy to normalize the data so the values run from 0% to 100%. Simply click Analyze,  choose the Normalize analysis., and define how 0% and 100% are defined. When fitting a dose-response curve, you can fit either the raw data or normalized data.

Notes:

•It is not necessary to normalize before fitting dose-response data. In many cases, it is better to show the actual data.

•You can only plot several different dose-response curves on one graph using one axis when they are comparable. If the different experiments measured different variables, normalizing puts them into comparable units. This can be useful.

•Whether or not you choose to normalize your data, you still need to choose how to fit the data. Do you want Prism to find best-fit values for the Top and Bottom plateaus? Or do you want those plateaus to be determined by control data?

•If you normalize your data, you can choose one of the normalized dose-response equations. These constrain the the curve to run from 0% to 100%. This kind of constraint only makes sense, when 0% and 100% are defined by good control data. If the definitions of 0% and 100% are ambiguous, then so is the definition of "50%", and thus the EC50 is also ambiguous.

•Just because you chose to normalize your data doesn't mean you must constrain the curve to run from 0 to 100%. You may prefer to have Prism fit those two plateaus.

•If you don't normalize your data, you can use the Constrain tab to fix Top and Bottom to values determined from control experiments. So the decision to constrain Top and Bottom is quite distinct from the decision to normalize your data before fitting.

•It is possible, and can be reasonable, to fix one of those parameters (Top or Bottom) to a constant value but not the other.

•If you normalize, don't also choose to differentially weight the data.