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