WNL in replicate BE [🇷 for BE/BA]
Dear bears,
it's not that easy. To quote from the manual:
Replicated crossover designs
For replicated crossover designs, the default model used in the Bioequivalence Wizard is the example model given for replicated designs in Appendix E of the FDA guidance:
Fixed effects model is:
Appendix E to FDA (2001) states:
The following illustrates an example of program statements to run the average BE analysis using PROC MIXED in SAS version 6.12, with SEQ, SUBJ, PER, and TRT identifying sequence, subject, period, and treatment variables, respectively, and Y denoting the response measure (e.g., log(AUC), log(Cmax)) being analyzed:
The Estimate statement assumes that the code for the T formulation precedes the code for the R formulation in sort order (this would be the case, for example, if T were coded as 1 and R were coded as 2). If the R code precedes the T code in sort order, the coefficients in the Estimate statement would be changed to -1 1.
In the Random statement, TYPE=FA0(2) could possibly be replaced by TYPE=CSH. This guidance recommends that TYPE=UN not be used, as it could result in an invalid (i.e., not nonnegative definite) estimated covariance matrix.
We have already discovered differences between SAS and WinNonlin in the evaluation of replicate designs.
No, that's another cup of tea! I was referring to EMA's guideline asking for ANOVA with fixed effects - which is not clever anyhow.
it's not that easy. To quote from the manual:
Replicated crossover designs
For replicated crossover designs, the default model used in the Bioequivalence Wizard is the example model given for replicated designs in Appendix E of the FDA guidance:
Fixed effects model is:
- DependentVariable = Intercept + Sequence + Formulation + Period
- Treatment
- Variance Blocking Variable set to Subject
- Type set to Banded No-Diagonal Factor Analytic with 2 factors
- Period
- Variance Blocking Variables set to Subject
- Group set to Treatment
- Type set to Variance Components
Appendix E to FDA (2001) states:
The following illustrates an example of program statements to run the average BE analysis using PROC MIXED in SAS version 6.12, with SEQ, SUBJ, PER, and TRT identifying sequence, subject, period, and treatment variables, respectively, and Y denoting the response measure (e.g., log(AUC), log(Cmax)) being analyzed:
PROC MIXED;
CLASSES SEQ SUBJ PER TRT;
MODEL Y = SEQ PER TRT/ DDFM=SATTERTH;
RANDOM TRT/TYPE=FA0(2) SUB=SUBJ G;
REPEATED/GRP=TRT SUB=SUBJ;
ESTIMATE 'T vs. R' TRT 1 -1/CL ALPHA=0.1;
The Estimate statement assumes that the code for the T formulation precedes the code for the R formulation in sort order (this would be the case, for example, if T were coded as 1 and R were coded as 2). If the R code precedes the T code in sort order, the coefficients in the Estimate statement would be changed to -1 1.
In the Random statement, TYPE=FA0(2) could possibly be replaced by TYPE=CSH. This guidance recommends that TYPE=UN not be used, as it could result in an invalid (i.e., not nonnegative definite) estimated covariance matrix.
We have already discovered differences between SAS and WinNonlin in the evaluation of replicate designs.
❝ I should search first, like this post of yours...
No, that's another cup of tea! I was referring to EMA's guideline asking for ANOVA with fixed effects - which is not clever anyhow.
—
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Helmut Schütz
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The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
![[image]](https://static.bebac.at/pics/Blue_and_yellow_ribbon_UA.png)
Helmut Schütz
![[image]](https://static.bebac.at/img/CC by.png)
The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes
Complete thread:
- Parallel bears meeting at random in infinity d_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 BEHelmut 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