WinNonlin 5.2.1 vs. 6 beta [🇷 for BE/BA]
Dear Helmut,
We used WNL v5.1.1 here. What WNL actually does in estimation of lambdaz is to include the last three data points first. And calculate its adjusted R2 for this step. Then include one more data point (i.e. the 4th data point from the last) and calculate adj. R2 again. If the difference between these two adj. R2s is less than or equal to 0.0001, then stop including further. If not, then include the 4th data points and add one more data points (i.e. the 5th data point from the last)... It will not stop until it reaches Cmax. That's why WNL may include Cmax to estimate lambdaz. WNL should stop including at the data point next to Cmax. The result you got from WNL v6 beta seems not meet its stop criterion. The adj. R2 for Ex. 04 including Cmax is 0.9941 win WNL v5.1.1 (or your WNL 5.2.1); however, v6 beta got 0.9937 only.
WLN v6 beta - Ex. 04 (quoted from your previous post)
WLN - Ex. 04
Yes, I agree with you. However, if you don't agree to include Cmax to estimate lambdaz, then WNL (v5.x.x) should not be used for this purpose.
We used WNL v5.1.1 here. What WNL actually does in estimation of lambdaz is to include the last three data points first. And calculate its adjusted R2 for this step. Then include one more data point (i.e. the 4th data point from the last) and calculate adj. R2 again. If the difference between these two adj. R2s is less than or equal to 0.0001, then stop including further. If not, then include the 4th data points and add one more data points (i.e. the 5th data point from the last)... It will not stop until it reaches Cmax. That's why WNL may include Cmax to estimate lambdaz. WNL should stop including at the data point next to Cmax. The result you got from WNL v6 beta seems not meet its stop criterion. The adj. R2 for Ex. 04 including Cmax is 0.9941 win WNL v5.1.1 (or your WNL 5.2.1); however, v6 beta got 0.9937 only.
WLN v6 beta - Ex. 04 (quoted from your previous post)
...
Final Parameters
---------------
Rsq 0.9968
Rsq_adjusted 0.9937
...
WLN - Ex. 04
Model: Plasma Data, Extravascular Administration
Number of nonmissing observations: 12
Dose time: 0.00
Dose amount: 80000.00
Calculation method: Linear Trapezoidal with Linear Interpolation
Weighting for lambda_z calculations: Uniform weighting
Lambda_z method: Find best fit for lambda_z, Log regression
Summary Table
-------------
Time Conc. Pred. Residual AUC AUMC Weight
-------------------------------------------------------------------------------
0.0000 0.0000 0.0000 0.0000
0.2500 30.10 3.763 0.9406
0.5000 211.0 33.90 15.07
0.7500 1221. 212.9 142.7
1.000 1485. 551.2 442.8
1.500 * 1837. 1816. 20.72 1382. 1503. 1.000
2.000 * 1615. 1704. -89.15 2245. 2999. 1.000
3.000 * 1621. 1500. 120.8 3863. 7046. 1.000
4.000 * 1411. 1321. 90.29 5379. 1.230e+004 1.000
8.000 * 763.0 793.3 -30.25 9727. 3.580e+004 1.000
12.00 * 424.0 476.4 -52.45 1.210e+004 5.818e+004 1.000
24.00 * 109.0 103.2 5.765 1.530e+004 1.044e+005 1.000
*) Starred values were included in the estimation of Lambda_z.
Final Parameters
---------------
Rsq 0.9951
Rsq_adjusted 0.9941
Corr_XY -0.9975
No_points_lambda_z 7
Lambda_z 0.1274
Lambda_z_lower 1.5000
Lambda_z_upper 24.0000
❝ For both cases we should consider contacting Pharsight’s support to clarify the issue.
Yes, I agree with you. However, if you don't agree to include Cmax to estimate lambdaz, then WNL (v5.x.x) should not be used for this purpose.
—
All the best,
-- Yung-jin Lee
bear v2.9.1:- created by Hsin-ya Lee & Yung-jin Lee
Kaohsiung, Taiwan https://www.pkpd168.com/bear
Download link (updated) -> here
All the best,
-- Yung-jin Lee
bear v2.9.1:- created by Hsin-ya Lee & Yung-jin Lee
Kaohsiung, Taiwan https://www.pkpd168.com/bear
Download link (updated) -> here
Thread locked
Complete thread:
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