Bioequivalence and Bioavailability Forum

udayk ☆ India, 2020-03-06 10:00 (1781 d 19:04 ago) Posting: # 21214 Views: 8,901	Outlier Test using R-Software and Phoenix WinNonlin software in the BE [Outliers] Post reply
	Dear All, Please help us for outlier test using Phoenix WinNonlin Software (Studentized residual method) and R-Software (code) in the Bioequivalence 2way cross design. Regards, UdayK. Edit: category changed and two successive posts merged [Ohlbe]

ping4santosh ★ India, 2020-03-11 15:58 (1776 d 13:06 ago) @ udayk Posting: # 21250 Views: 5,970	Outlier Test using R-Software and Phoenix WinNonlin software in the BE Post reply
	Hi Uday, Please let me know more details. I can help you with outliers treatment. Best, SKM

Helmut
★★★

Vienna, Austria,
2020-03-13 12:21
(1774 d 16:43 ago)

@ udayk
Posting: # 21267
Views: 5,960

Studentized & standardized residuals

Post reply

Hi UdayK,

❝ […] outlier test using Phoenix WinNonlin Software (Studentized residual method) and R-Software (code) in the Bioequivalence 2way cross design.

“Studentized residual” is an ambiguous term. There are two flavors:

Internally studentized (aka “standardized”), where
\(\widehat{\sigma}^2={1 \over n-m}\sum_{j=1}^n \widehat{\varepsilon\,}_j^{\,2}\)
Externally studentized, where
\(\widehat{\sigma}_{(i)}^2={1 \over n-m-1}\sum_{\begin{smallmatrix}j = 1\\j \ne i\end{smallmatrix}}^n \widehat{\varepsilon\,}_j^{\,2}\)

#1 is easily accessible in Phoenix WinNonlin (divide the raw residual by the standard error). Assess only the first period. The value of the other period has the same value but with the opposite sign and is therefore, not informative. No idea how to get #2 In WinNonlin. Ask at Certara’s forum.
Both are easily obtained in SAS, R,…

In R for the fixed effects model (untested!):

mod <- lm(log(PK) ~ sequence + subject%in%sequence + period + treatment, data = yourdata) res1 <- rstandard(mod) # 1. Internally studentized (standardized) res2 <- rstudent(mod) # 2. Externally studentized

Note that #2 is in general more restrictive than #1, i.e., it might be that you detect more outliers.
BTW, which outlier test are you thinking of? See also there and this thread at stackoverflow.

—
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes

ping4santosh ★ India, 2020-03-14 15:52 (1773 d 13:12 ago) @ udayk Posting: # 21269 Views: 5,827	Outlier Test using R-Software: Lund test Post reply
	Hi Uday, Try Lund test in R package. If you want, I can help you out. Cheers, SKM

udayk ☆ India, 2020-03-20 08:05 (1767 d 20:59 ago) @ ping4santosh Posting: # 21288 Views: 5,734	Outlier Test using R-Software: Lund test Post reply
	Hi SKM, Please help us. Regards, Uday

ping4santosh ★ India, 2020-03-20 11:07 (1767 d 17:57 ago) (edited on 2020-03-21 03:37) @ udayk Posting: # 21289 Views: 5,632	Outlier Test using R-Software: Lund test Post reply
	❝ Please help us. Hi Uday: I shall need access to the data and the background info. Cheers, SKM NB: my email ID is my ID here in Gmail.

yjlee168
★★★

Kaohsiung, Taiwan,
2020-03-20 15:45
(1767 d 13:19 ago)

@ udayk
Posting: # 21291
Views: 5,658

bear for outlier detection analysis?

Post reply

Hi udayk,

You may consider bear - an R package if you like. With bear, you can do outlier detection analysis (ODA) for a 2x2x2 BE study. Bear provides the following ODAs:

Test for Normality (Pearson): studentized intra-subject residuals & studentized inter-subject residuals;
Hotelling T² with Chi-square Test;
Test for Equality of Intra-subject Variabilities between Formulations; and
Cook's distance.

For more info, please see my signature panel.

[image]

—
All the best,
-- Yung-jin Lee
bear v2.9.2:- created by Hsin-ya Lee & Yung-jin Lee
Kaohsiung, Taiwan https://www.pkpd168.com/bear
Download link (updated) -> here

xtianbadillo
☆

Mexico,
2024-12-16 21:44
(35 d 07:20 ago)

@ yjlee168
Posting: # 24317
Views: 809

bear for outlier detection analysis?

Post reply

Good morning,

I was performing some residual analyses while also checking if my bear 2.9.2 was correctly installed on my PC with Windows 10. I reviewed the version 2 validation file and compared it against the residuals, and I discovered that there is an error in my system, although I haven’t yet identified what it is. The residuals I obtain are different from the values reported in the validation. Using the same dataset, I ran it in R to obtain the residuals, and they match those in the validation. Later, I will run an older version I have on Windows 7.

I am sharing the results.

From by Hsin-ya Lee & Yung-jin Lee (09/07/2009) Validation of bear using WNL & SAS
VERSION 2 Page 122 of 162

Intra-subject and Inter-subject Residuals
--------------------------------------------------------------------------
subj Obs Exp Intra Stud_Intra Inter Stud_Inter
1 1 7.398174 7.491068 -0.092894 -1.126022 0.138200 0.723698
2 2 7.300473 7.409175 -0.108702 -1.317638 0.098454 0.515564
3 3 7.636752 7.622009 0.014743 0.178707 0.400081 2.095061
4 4 7.225481 7.311596 -0.086115 -1.043844 -0.096705 -0.506404
5 5 7.233455 7.352791 -0.119336 -1.446539 -0.138354 -0.724503
6 6 7.470794 7.400282 0.070512 0.854712 0.080667 0.422421
7 7 7.404279 7.441728 -0.037449 -0.453938 0.039519 0.206946
8 8 7.569928 7.479503 0.090425 1.096082 0.239110 1.252122
9 9 7.472501 7.445906 0.026595 0.322378 0.047874 0.250697
10 10 7.235619 7.269715 -0.034096 -0.413295 -0.180467 -0.945031
11 11 7.427739 7.288976 0.138763 1.682023 -0.265986 -1.392859
12 12 7.340836 7.235661 0.105175 1.274886 -0.248576 -1.301690
13 13 7.380879 7.311301 0.069578 0.843390 -0.221335 -1.159040
14 14 7.376508 7.413706 -0.037198 -0.450902 0.107516 0.563018

From Bear 2.9.2 Using Single2x2x2_stat_demo Statistical Analysis Only

* Intra-subject and Inter-subject Residuals *
---------------------------------------------
subj Obs Exp Intra Stud_Intra Inter Stud_Inter
1 7.400000 7.490714 -0.090714 -1.100692 0.138571 0.727189
2 7.300000 7.410714 -0.110714 -1.343364 0.098571 0.517279
3 7.640000 7.620714 0.019286 0.234005 0.398571 2.091605
4 7.230000 7.315714 -0.085714 -1.040024 -0.091429 -0.479795
5 7.230000 7.350714 -0.120714 -1.464700 -0.141429 -0.742182
6 7.470000 7.400714 0.069286 0.840686 0.078571 0.412324
7 7.400000 7.440714 -0.040714 -0.494011 0.038571 0.202413
8 7.570000 7.480714 0.089286 1.083358 0.238571 1.251964
9 7.470000 7.445714 0.024286 0.294673 0.048571 0.254891
10 7.240000 7.270714 -0.030714 -0.372675 -0.181429 -0.952092
11 7.430000 7.290714 0.139286 1.690039 -0.261429 -1.371913
12 7.340000 7.235714 0.104286 1.265362 -0.251429 -1.319435
13 7.380000 7.310714 0.069286 0.840686 -0.221429 -1.162003
14 7.380000 7.415714 -0.035714 -0.433343 0.108571 0.569756

From Bear 2.9.2 Using Single2x2x2_stat_demo NCA ->Statistical Analysis Only

---------------------------------------------

* Intra-subject and Inter-subject Residuals *
---------------------------------------------
subj Obs Exp Intra Stud_Intra Inter Stud_Inter
1 7.398174 7.491068 -0.092894 -1.063472 0.138200 0.686507
2 7.300473 7.397679 -0.097206 -1.112839 0.075462 0.374856
3 7.636752 7.622009 0.014743 0.168782 0.400081 1.987400
4 7.225481 7.300100 -0.074618 -0.854246 -0.119697 -0.594594
5 7.233455 7.352792 -0.119336 -1.366186 -0.138354 -0.687270
6 7.470794 7.388785 0.082008 0.938847 0.057674 0.286497
7 7.404279 7.441728 -0.037449 -0.428723 0.039519 0.196312
8 7.569928 7.468007 0.101920 1.166805 0.216118 1.073564
9 7.472501 7.445906 0.026595 0.304466 0.047874 0.237815
10 7.235619 7.258219 -0.022600 -0.258728 -0.203459 -1.010680
11 7.427739 7.288975 0.138764 1.588593 -0.265986 -1.321286
12 7.340836 7.224164 0.116671 1.335678 -0.271569 -1.349016
13 7.380879 7.311301 0.069578 0.796540 -0.221334 -1.099478
14 7.376508 7.482683 -0.106175 -1.215516 0.245470 1.219372

ejemplo <- lm (lnCmax~Secuencia+Sujeto*Secuencia+Tratamiento+Periodo , data=datos)
anova(ejemplo)

valores_predichos <- round(fitted(ejemplo),4)
residuales <- round(residuals(ejemplo),4)
residuales_std <- round(rstandard(ejemplo),4)
residuales_stu <- round(rstudent(ejemplo),4)

valores_observados valores_predichos residuales residuales_std residuales_stu
1 7.4611 7.3682 0.0929 1.1260 1.1400
2 7.3005 7.4092 -0.1087 -1.3176 -1.3641
3 7.4844 7.4991 -0.0147 -0.1787 -0.1713
4 7.2255 7.3116 -0.0861 -1.0438 -1.0481
5 7.3492 7.2299 0.1193 1.4465 1.5242
6 7.4708 7.4003 0.0705 0.8547 0.8444

Outlier Test using R-Software and Phoenix WinNonlin software in the BE [Outliers]

Outlier Test using R-Software and Phoenix WinNonlin software in the BE

Studentized & standardized residuals

Outlier Test using R-Software: Lund test

Outlier Test using R-Software: Lund test

Outlier Test using R-Software: Lund test

bear for outlier detection analysis?

bear for outlier detection analysis?