Sandwich - Simsalabim [🇷 for BE/BA]
Dear Helmut,
----- Sandwich -----
To cite myself near the beginning of this thread: "I would go for a parallel groups study with exactly 2 groups with the 'simple' t-test (Welch variant ...)". Simple was here meant with respect to the lme() model used in bear.
But I'm seeking for something that can be generalized easily to more then 2 groups. Pairwise tests I'm not so happy with.
Eventually this (recently discovered) could be a way:
Herberich E, Sikorski J, Hothorn T (2010)
"A Robust Procedure for Comparing Multiple Means under Heteroscedasticity in Unbalanced Designs"
PLoS ONE 5(3): e9788. doi:10.1371/journal.pone.0009788
online resource
Easy to implement in R, only a couple of code lines.
But I was not a sandwich gourmet up to now .
----- Simsalabim -----
<nitpicking>
If you are really interested in dealing with log-transformed metrics as lnPK suggests I would suggest you the following modification:
Of course this will not affect the comparison between t-test with equal variances and Welch t-test I think.
</nitpicking>
----- Sandwich -----
❝ you have already outed yourself as a passionate wheel-reinventer, but why don't you simply go with the Welch-test (orwhatsover for unequal variances)?
To cite myself near the beginning of this thread: "I would go for a parallel groups study with exactly 2 groups with the 'simple' t-test (Welch variant ...)". Simple was here meant with respect to the lme() model used in bear.
But I'm seeking for something that can be generalized easily to more then 2 groups. Pairwise tests I'm not so happy with.
Eventually this (recently discovered) could be a way:
Herberich E, Sikorski J, Hothorn T (2010)
"A Robust Procedure for Comparing Multiple Means under Heteroscedasticity in Unbalanced Designs"
PLoS ONE 5(3): e9788. doi:10.1371/journal.pone.0009788
online resource
Easy to implement in R, only a couple of code lines.
But I was not a sandwich gourmet up to now .
----- Simsalabim -----
<nitpicking>
❝ ...
❝ lnPKT <- rnorm(n=nT,mean=MeanT,sd=CVT*MeanT)
❝ lnPKR <- rnorm(n=nR,mean=MeanR,sd=CVR*MeanR)
❝ ...
If you are really interested in dealing with log-transformed metrics as lnPK suggests I would suggest you the following modification:
# short hand function
CV2sd <- function(CV) return(sqrt(log(1.0 + CV^2)))
...
lnPKT <- rlnorm(n=nT, mean=log(MeanT), sd=CV2sd(CVT))
lnPKR <- rlnorm(n=nR, mean=log(MeanR), sd=CV2sd(CVR))
Of course this will not affect the comparison between t-test with equal variances and Welch t-test I think.
</nitpicking>
—
Regards,
Detlew
Regards,
Detlew
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 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 - Simsalabimd_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 - Simsalabimd_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