WinNonlin 5.2.1 vs. 6 beta --> new finding [🇷 for BE/BA]

posted by yjlee168 Homepage – Kaohsiung, Taiwan, 2008-09-30 10:10 (5681 d 03:08 ago) – Posting: # 2440
Views: 55,878

Dear Helmut,

I tried to eliminate Cmax from data point selection for lambdaz estimation with Ace's method (codes/algorithms). I found something interesting. WinNonlin (WNL) v6 beta does actually exclude Cmax from point selection! I got the same results obtained from modified Ace's method as WNL v6 beta did (your previous post) with Ex.04 (and probably Ex. 06, too).
In my previous post, the algorithm I mentioned about data point selection by WNL may not be correct. As the matter of the fact, WNL v5.x.x calculates each adj. R2 value starting with the last three data points, then the last 4 data points, the last 5 data points... until including Cmax. Finally it picks the dataset which had the maximum adj. R2 value among others to do linear regression for estimation of lambdaz. However, with WNL v6 beta, the calculation or searching will stop at the data point next to Cmax. So I changed the line of for (i in (nrow(dat)-2):(which.max(dat$conc+1))) with Ace's method to for (i in (nrow(dat)-2):(which.max(dat$conc))+1) (R scripts). I found that I could successfully eliminate Cmax from data points selection. Then the results surprised me! We had the same result as those you got from WNL v6 beta! See the following (pasted from R console).

Ex. 04
Call:
lm(formula = log(conc) ~ time, data = dat[(nrow(dat) - n_lambda +
    1):nrow(dat), ])

Residuals:
      10       11       12
 0.04664 -0.06218  0.01555

Coefficients:
             Estimate Std. Error t value Pr(>|t|)   
(Intercept)  7.548030   0.108820   69.36  0.00918 **
time        -0.119676   0.006731  -17.78  0.03577 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.07927 on 1 degrees of freedom
Multiple R-squared: 0.9968,     Adjusted R-squared: 0.9937
F-statistic: 316.1 on 1 and 1 DF,  p-value: 0.03577

Ex. 06
Call:
lm(formula = log(conc) ~ time, data = dat[(nrow(dat) - n_lambda +
    1):nrow(dat), ])

Residuals:
       8        9       10       11       12
 0.01676  0.03580  0.04976 -0.15536  0.05303

Coefficients:
             Estimate Std. Error t value Pr(>|t|)   
(Intercept)  7.962810   0.076072  104.67 1.92e-06 ***
time        -0.153656   0.005981  -25.69 0.000129 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1016 on 3 degrees of freedom
Multiple R-squared: 0.9955,     Adjusted R-squared: 0.994
F-statistic: 660.1 on 1 and 3 DF,  p-value: 0.0001293

Ex. 05 had the same result as the that obtained from previous version. It happened when the dataset that included Cmax did not have the maximum adj. R2. If you like, you can test Ex. 06 (WNL v5.x.x included the Cmax with this example, too) with WNL v6 beta to see if you can get the exact same results as ours. It should, I guess. We will still test more data set. If no other errors, we will include this method into bear. Time to move to TTT method coding...
—
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
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