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Email: Scientific graphing,curve fitting and statistics      64 Bit

A0:目前原廠並無提供網路版本,僅販售單機版及多人授權版
We no longer offer a concurrent use network license.
Only individual single user licenses are available for resale.
An individual license may be installed on one computer.

Q1：我的圖想從 0~100 ,但 資料是 25.2~241.2 怎麼辦?
A1：使用Normalize就OK

Analyze
Data Manipulations
Normalize

0  %
100 %

Q2：請問我Normalize後有data?
A2：Yes, 所以您可以求Normalize後的Mean, SD.

Q3：網路版如何安裝?
A3：請洽本公司

A4：請洽本公司

A5：因為要節能減碳,所以只有PDF檔

A6：The application had encountered a problem and needs to close.
Error#: 0001:000-4

IC50, EC50和ED50有何不同?
A8：The differences are just nomenclature, and not conceptual.
"EC" means effective concentration, and is used for dose-response curves that go up hill. Sometimes it is also used for a curve that goes downhill.
"IC" means inhibitory concentration, so is used for dose-response curves that go downhill, because the drug inhibits a response.
In both cases, "C" stands for concentration. In some experiments, you vary the administered dose, but don't know the effective concentration. In these cases, the midpoint is called the ED50.

Q9：Do you have to transform  X data into log format?
If I transform  X data into log format.
I have a big problem. Since My X had 0 in concentration
A9：How to deal with zero dose:
When performing a dose-response curve, most investigators measure response at zero dose. Handling this data point is not straightforward because we recommend entering data with X equal to the logarithm of concentration, but the logarithm of zero is undefined.

We suggest entering a low concentration (perhaps one or two log units lower than your lowest concentration) instead of zero. If you enter a concentration so low that is has essentially no effect on the response, this method works fine and gives the correct results. But some people are wary about analyzing data this way, feeling that it is 'cheating' to enter some arbitrary (but very low) value rather than zero for the blank.

One alternative is simply to omit the data for the zero concentration. But this leaves out useful data, and in some cases (where the lowest concentration you use has a detectable response above the baseline), leaving out the zero value can lead to less accurate fits.

Here is an alternative that takes into account the zero value without 'cheating'.

Step 1. Enter your data as usual, with X as log(concentration) and using a low log(concentration) for the zero value. You can choose any value you want to enter as zero.

Step 2. Fit your data to this user-defined equation:

Baseline=IF(HillSlope>0.0, Bottom, Top)
Response=Bottom + (Top-Bottom)/(1+10^((LogEC50-X)*HillSlope))
Y=IF(X=ZeroValue,Baseline,Response)

The first line defines the baseline of the curve, the response with no added dose. If the HillSlope is positive, the curve goes uphill so the response with zero added dose is the bottom plateau, so we set baseline equal to the parameter Bottom. If the HillSlope is negative, then the curve is a downhill inhibition curve, so the response with no added drug is the top platea and we set the baseline equal to the paramter Top.

The second line defines the response for nonzero doses. It is the same equation as Prism's built-in variable slope dose-response curve.

The final line sets Y equal to the baseline value when X equals ZeroValue and otherwise sets Y equal to the response. So when you enter that special ZeroValue for X, Prism never computes the usual dose-response equation but instead defines Y to equal the baseline. ZeroValue is not used as a concentration in a computation, but rather as a "flag" to tell the IF-THEN statement to define Y to be equal to the baseline  rather than equal to the result of the dose-response equation.

Step 3. Assign rules for initial values.

• Set Bottom equal to 1*Ymin.
• Set Top to 1*Ymax.
• Set logEC50 to the value of X at Ymid.
• Set HillSlope equal to SGN(YatXmax - YatXmin) so it equals 1.0 or -1.0 depending on whether your curve goes up or down.
• Set ZeroValue to 0.0. (You'll constraint ZeroValue to equal a constant value in the next step, so this initial value will be ignored, but you have to enter something to close the dialog.

Step 4. Back on the main nonlinear regression parameters dialog, go to the constraints tab and constrain the parameter ZeroValue to be equal to a constant value equal to whatever value you have entered for X when the dose was zero.

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Does this method give more accurate answers than the standard method? Not in most cases. So long as you enter a 'concentration' more than three log units below the EC50 (and assuming a Hill Slope not too far from 1.0), the usual method works fine because the dose-response equation computes a response indistinguishable from the baseline when the dose is less than 0.001 times the EC50.

The advantage of this new method is that it is concepturally pure. You don't have to pretend that a zero dose is really something else. It gets around the problem that the log of zero is undefined by using an IF-THEN relationship to define the response one way for the zero dose and another way for all other doses. The disadvantages are that you have to enter a user-defined equation, the syntax is confusing at first, and you may find it hard to explain to others exactly what you did.