Power Utility Pak Version 7 Pup V7 1 🤘
Power Utility Pak Version 7 Pup V7 1
9.7.2 modeling the su50 hypothesis with maximum likelihood estimates the su50 is not a constant and depends on the fraction of the total activity that is deposited in the mediastinum. therefore, it is more appropriate to model the suv50 as a variable for the su50, rather than use a constant. in this way, the maximum likelihood estimation method described in chapter 3 can be applied to define a function for the su50. that is, you are fitting a curve to the data using the maximum likelihood method. since the medians are not normally distributed, you have to fit a nonparametric function to the data. for that reason, the spss curve fitting procedure is used in this example. the curve fitted is: su50=3.918+0.254 (suv max 1.6+suvmax/1.6)
the curve fit that is selected is the linear, and the gradient gives a value for the constant. there is a value on this curve that you can evaluate to obtain the su50, and that is 3.918. that is the value that would be obtained if there were only one metric unit of activity on the pet, and that is the su50 for the suvmax of 1.6. using the fiducial values and the curve fit, create a new variable that is the su50 for the suvmax, and call this variable su50_nomedian. specify that the units of this new variable are suvmax, and that its units are nuclide-grams/ml. the curve fit coefficients are expressed as: (a) 0.254, and (b) 1. all the data can now be entered into the generated model.
1 3. using the cell a1, type the following formula =exp(.75) and press enter. this simple formula results in a 67.5 value in cell a2. the exp() function results in a 64-bit result, so the range a2:a4 now contains 64.7. you can check the result by opening cell a2. the editor shows the formula as (=exp(0.75)). press enter to select the cell. the exp() function appears in the function list.
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