Parallel bears meeting at random in infinity [🇷 for BE/BA]
Dear all, especially dear bears,
since I'm very interested in using R for evaluation of BE studies I had a closer look at the bear code for parallel group studies (code inspection as part of validation
).
If I got it right the code used is (f.i. AUC log-transformed):
I am wondering where this code comes from, what this code does an why it works anyhow
.
IMHO this model, one (fixed) effect for the treatment and one (random) effect for the subjects must be over-specified. We have only one value for a distinct subject treated with Test or Reference and thus we are not able to separate this uniquely into 2 effects, one part for treatment and one attributed to the subject.
Nevertheless the lme() call produces a result.
Try it out in bear.
If I follow the strange and crude EMA suggestion and use all effects as fixed
with the bear built-in dataset for parallel groups I get the anticipated result:
BTW: I would go for a parallel groups study with exactly 2 groups with the 'simple' t-test (Welch variant as described by Helmut long ago)
.
Is anybody out there who knows a generalization of the Welch test to more than 2 groups? Any hint would be very appreciated!
Or could we use pairwise Welch t-tests for that?
since I'm very interested in using R for evaluation of BE studies I had a closer look at the bear code for parallel group studies (code inspection as part of validation

If I got it right the code used is (f.i. AUC log-transformed):
lme(lnAUC0t ~ drug, random=~1|subj, data=TotalData, method="REML" )
I am wondering where this code comes from, what this code does an why it works anyhow

IMHO this model, one (fixed) effect for the treatment and one (random) effect for the subjects must be over-specified. We have only one value for a distinct subject treated with Test or Reference and thus we are not able to separate this uniquely into 2 effects, one part for treatment and one attributed to the subject.
Nevertheless the lme() call produces a result.
Try it out in bear.
If I follow the strange and crude EMA suggestion and use all effects as fixed
lmModel <- lm(lnAUC0t ~ drug + subj, data=TotalData)
with the bear built-in dataset for parallel groups I get the anticipated result:
>summary(lmModel)
Call:
lm(formula = lnAUC0t ~ drug + subj, data = TotalData)
Residuals:
ALL 20 residuals are 0: no residual degrees of freedom! (over-specified!)
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.3199 NA NA NA
drug2 -0.0622 NA NA NA
subj2 -0.5366 NA NA NA
[...]
>anova(lmModel)
Analysis of Variance Table
Response: lnAUC0t
Df Sum Sq Mean Sq F value Pr(>F)
drug 1 0.00127 0.001270
subj 18 1.17959 0.065533
Residuals 0 0.00000
BTW: I would go for a parallel groups study with exactly 2 groups with the 'simple' t-test (Welch variant as described by Helmut long ago)

Is anybody out there who knows a generalization of the Welch test to more than 2 groups? Any hint would be very appreciated!
Or could we use pairwise Welch t-tests for that?
—
Regards,
Detlew
Regards,
Detlew
Complete thread:
- Parallel bears meeting at random in infinityd_labes 2010-04-22 11:43 [🇷 for BE/BA]
- Parallel bears meeting at random in infinity ElMaestro 2010-04-22 12:53
- Parallel groups in bear - CIs d_labes 2010-04-22 14:00
- Parallel groups in bear - CIs ElMaestro 2010-04-22 21:47
- Parallel groups in bear - CIs d_labes 2010-04-23 09:09
- Parallel groups in bear - CIs yjlee168 2010-04-25 23:29
- Parallel groups in bear - CIs ElMaestro 2010-04-22 21:47
- Parallel groups in bear - CIs d_labes 2010-04-22 14:00
- Parallel bears meeting at random in infinity yjlee168 2010-04-22 23:09
- Modelling Parallel bears d_labes 2010-04-23 09:12
- Modelling Parallel bears yjlee168 2010-04-23 21:14
- Validating vs. WinNonlin... Helmut 2010-04-24 00:28
- Validating vs. WinNonlin... yjlee168 2010-04-24 19:36
- Validating vs. WinNonlin... yjlee168 2010-04-26 00:09
- Validating vs. WinNonlin... Helmut 2010-04-26 01:29
- WNL in replicate BE yjlee168 2010-04-26 08:59
- WNL in replicate BE Helmut 2010-04-26 16:15
- WNL in replicate BE yjlee168 2010-04-26 08:59
- Validating vs. WinNonlin... Helmut 2010-04-26 01:29
- Modelling Parallel bears yjlee168 2010-04-25 19:34
- Modelling Parallel bears ElMaestro 2010-04-25 20:40
- Dataset Helmut 2010-04-25 22:38
- Dataset yjlee168 2010-04-25 22:44
- Dataset Helmut 2010-04-26 01:13
- Dataset yjlee168 2010-04-26 08:16
- NCA → Statistical analysis for parallel study Helmut 2010-04-26 13:12
- NCA → Statistical analysis for parallel study yjlee168 2010-04-26 18:43
- NCA → Statistical analysis for parallel study Helmut 2010-04-26 13:12
- Dataset yjlee168 2010-04-26 08:16
- Dataset Helmut 2010-04-26 01:13
- dilemma yjlee168 2010-04-26 08:41
- Equal variances d_labes 2010-04-26 09:04
- Equal variances yjlee168 2010-04-26 09:22
- GLM = Equal variances d_labes 2010-04-26 13:29
- GLM = Equal variances Helmut 2010-04-26 14:45
- I'm a believer d_labes 2010-04-26 15:58
- I'm a believer Helmut 2010-04-26 16:31
- I'm a believer d_labes 2010-04-26 15:58
- GLM = Equal variances Helmut 2010-04-26 14:45
- GLM = Equal variances d_labes 2010-04-26 13:29
- Equal variances Helmut 2010-04-26 12:55
- gls() for unequal variances? d_labes 2010-04-26 16:36
- gls() for unequal variances? Helmut 2010-04-26 17:00
- Sims Helmut 2010-04-27 01:36
- Sandwich - Simsalabim d_labes 2010-04-28 10:58
- Sandwich - Simsalabim Helmut 2010-04-28 14:19
- parametrization of R function rlnorm martin 2010-05-02 18:22
- Mean of log-normal d_labes 2010-05-03 16:22
- parametrization of R function rlnorm ElMaestro 2013-07-26 21:42
- Martin‽ Helmut 2013-07-28 02:01
- Sandwich - Simsalabim d_labes 2010-04-28 10:58
- gls() for unequal variances? d_labes 2010-04-26 16:36
- Equal variances yjlee168 2010-04-26 09:22
- Dataset yjlee168 2010-04-25 22:44
- Validating vs. WinNonlin... Helmut 2010-04-24 00:28
- Modelling Parallel bears yjlee168 2010-04-23 21:14
- Modelling Parallel bears d_labes 2010-04-23 09:12
- Parallel bears meeting at random in infinity ElMaestro 2010-04-22 12:53