Hello NONMEM Users,
 
I try to make sense of the results and one of the ways to do it is to compare the same or similar models across software packages.  5x5 full omega matrix is used because it was prohibitive to remove some insignificant correlations from the matrix without removing significant correlations (All recommended ways to do it were tested. Diagonal omega was also tested, of course).  Adding correlations has little effect on PK parameters, but it has some effect on simulations. 
 
NONMEM provides all eigenvalues in one pocket.  Here is an example. 
 
************************************************************************************************************************
 ********************                                                                                ********************  ********************                STOCHASTIC APPROXIMATION EXPECTATION-MAXIMIZATION               ********************  ********************                    EIGENVALUES OF COR MATRIX OF ESTIMATE (S)                   ********************  ********************                                                                                ********************
 
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             1         2         3         4         5         6         7         8         9        10        11        12              13        14        15        16        17        18        19        20        21        22        23
 
         3.36E-05  5.69E-03  3.40E-02  6.32E-02  9.19E-02  1.24E-01  1.53E-01  2.79E-01  3.20E-01  4.32E-01  5.74E-01  6.45E-01           7.25E-01  7.67E-01  9.73E-01  1.08E+00  1.42E+00  1.63E+00  1.86E+00  2.14E+00  2.31E+00  3.12E+00  4.26E+00
 
Monolix provides them in 3 pockets:
 
PK parameters: Eigenvalues (min, max, max/min): 0.22  2  9.2
OMEGA (diagonal) and SIGMA: Eigenvalues (min, max, max/min): 0.66  1.5  2.2
OMEGA (correlations):  Eigenvalues (min, max, max/min): 0.097  2.5  25
 
Even though the results look similar, eigenvalues look different.  Taking into account that max/min ratio is frequently reported, it is important to understand the difference.  It almost look like different sets of parameters are estimated separately in the Monolix example, which most likely is not the case.  Even if we combine all eigenvalues in one pocket, max/min looks good.   It is impressive that max/min ratio for OMEGA correlations may look OK even though there are small correlations such as -0.0921, SE=0.064, RSE=70%.
 
What is the best way to report estimate and report max/min ratios?
 
Take care,
Pavel

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