s2wR from ISC in FDA approach [General Statistics]
Dear ElMaestro!
If you have a look at the SAS code in the progesterone guidance you will discover that s2wR is not obtained from the REML covariance matrix but rather from an ISC evaluation. Means you calculate R-R within the subjects and use that difference in an ANOVA with the sequence as the soley effect.
The REML estimates don't play a role, since a mixed model only comes into the play if CVwR is <=30% and conventional ABE has to be used. Here you may get s2wR from the REML covariance matrix but it is not used in ABE.
That's one of the curiosities of the FDA approach, among others.
Here you err. The EMA approach calls for an evaluation via lm(), GLM or comparable using the R(eference) data only with the effects period and subject (sequence may be also included but doesn't change the estimate of s2wR). This gives some different results to an estimation via ISC as described above. Try it.
BTW: Have a look into
❝ ...
❝ ❝ Moreover the FDA evaluation uses intra-subject constrasts to evaluate s2wR
❝ What does this mean? Don't you usually get it from the REML covarance matrix?
If you have a look at the SAS code in the progesterone guidance you will discover that s2wR is not obtained from the REML covariance matrix but rather from an ISC evaluation. Means you calculate R-R within the subjects and use that difference in an ANOVA with the sequence as the soley effect.
The REML estimates don't play a role, since a mixed model only comes into the play if CVwR is <=30% and conventional ABE has to be used. Here you may get s2wR from the REML covariance matrix but it is not used in ABE.
That's one of the curiosities of the FDA approach, among others.
❝ I think EMA's approach can be condensed into intra-Subject contrasts to derive S2wR from completers.
Here you err. The EMA approach calls for an evaluation via lm(), GLM or comparable using the R(eference) data only with the effects period and subject (sequence may be also included but doesn't change the estimate of s2wR). This gives some different results to an estimation via ISC as described above. Try it.
BTW: Have a look into
Implementation_scaledABE_sims.pdf
in the doc subdirectory of R package PowerTOST
. There you will find a cookbook manner description of the computations involved in the EMA or FDA approaches for scaled ABE.—
Regards,
Detlew
Regards,
Detlew
Complete thread:
- Confidence Interval for Transformed data Unbalanced study usfda_emea 2007-03-06 14:39 [General Statistics]
- CI for Transformed data Unbalanced study Helmut 2007-03-06 16:46
- CI for Transformed data Unbalanced study drshiv 2007-03-06 18:49
- CI for Transformed data Unbalanced study usfda_emea 2007-03-07 06:21
- CI for Transformed data Unbalanced study Helmut 2007-03-07 12:04
- CI for Transformed data Unbalanced study usfda_emea 2007-03-07 12:34
- CI for Transformed data Unbalanced study Ohlbe 2014-07-30 15:04
- LSM limbo Helmut 2014-07-31 02:54
- LSM limbo Ohlbe 2014-07-31 09:47
- 90% CI limbo VStus 2017-03-01 14:07
- RSABE ⇒ ABEL Helmut 2017-03-03 13:34
- RSABE ⇒ ABEL VStus 2017-03-03 15:31
- s2wR != mse, FDA != EMA d_labes 2017-03-04 14:49
- s2wR != mse, FDA != EMA ElMaestro 2017-03-04 19:13
- s2wR from ISC in FDA approachd_labes 2017-03-05 12:18
- s2wR from ISC in FDA approach ElMaestro 2017-03-05 13:50
- s2wR from ISC in FDA approachd_labes 2017-03-05 12:18
- s2wR != mse, FDA != EMA VStus 2017-03-04 21:41
- s2wR != mse, FDA != EMA Helmut 2017-03-06 13:40
- s2wR != mse, FDA != EMA ElMaestro 2017-03-04 19:13
- s2wR != mse, FDA != EMA d_labes 2017-03-04 14:49
- RSABE ⇒ ABEL VStus 2017-03-03 15:31
- RSABE ⇒ ABEL Helmut 2017-03-03 13:34
- LSM limbo Helmut 2014-07-31 02:54
- CI for Transformed data Unbalanced study Helmut 2007-03-07 12:04
- CI for Transformed data Unbalanced study Helmut 2007-03-06 16:46