Bioequivalence and Bioavailability Forum

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