Hi All,
I plan to identify metabolite levels that differ between individuals
with various retinopathy outcomes (DR or noDR). I plan to model
metabolite levels using linear mixed models ref as implemented in
lmm2met software. The model covariates will include: age, sex, SV1,
SV, and disease_condition.
The random effect is subject variation (ID)
Disease condition is the fixed effect because I am interested in
metabolite differences between those disease conditions.
This command will build a model for each metabolite:
fitMet = fitLmm(fix=c('Sex','Age','SV1,'SV2','disease_condition'),
random='(1|ID)', data=df, start=10)
SV1 and SV2 are surrogate variables (numerical values)
Next I need to calculate the power of my study. Let's say that I have
1,172 individuals total in the study, from which 431 are DR. Let's say
that I would like to determine the power of this study given the
effect size of 0.337.
I know about SIMR software in R but I am not sure how to apply it to
my study design.
I looked at this paper:
https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12504
But I am not sure how to adapt the code given in the tutorial so that
it is matching to mine design.
Can you please help,
Thanks
Ana
calculate power-linear mixed effect model
4 messages · Ana Marija, Bert Gunter
Wrong list! Post on r-sig-mixed-models, not here. Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Fri, Sep 17, 2021 at 12:22 PM Ana Marija <sokovic.anamarija at gmail.com> wrote:
Hi All,
I plan to identify metabolite levels that differ between individuals
with various retinopathy outcomes (DR or noDR). I plan to model
metabolite levels using linear mixed models ref as implemented in
lmm2met software. The model covariates will include: age, sex, SV1,
SV, and disease_condition.
The random effect is subject variation (ID)
Disease condition is the fixed effect because I am interested in
metabolite differences between those disease conditions.
This command will build a model for each metabolite:
fitMet = fitLmm(fix=c('Sex','Age','SV1,'SV2','disease_condition'),
random='(1|ID)', data=df, start=10)
SV1 and SV2 are surrogate variables (numerical values)
Next I need to calculate the power of my study. Let's say that I have
1,172 individuals total in the study, from which 431 are DR. Let's say
that I would like to determine the power of this study given the
effect size of 0.337.
I know about SIMR software in R but I am not sure how to apply it to
my study design.
I looked at this paper:
https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12504
But I am not sure how to adapt the code given in the tutorial so that
it is matching to mine design.
Can you please help,
Thanks
Ana
______________________________________________ R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Thank you so much for that info!
On Fri, Sep 17, 2021 at 3:06 PM Bert Gunter <bgunter.4567 at gmail.com> wrote:
Wrong list! Post on r-sig-mixed-models, not here. Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Fri, Sep 17, 2021 at 12:22 PM Ana Marija <sokovic.anamarija at gmail.com> wrote:
Hi All,
I plan to identify metabolite levels that differ between individuals
with various retinopathy outcomes (DR or noDR). I plan to model
metabolite levels using linear mixed models ref as implemented in
lmm2met software. The model covariates will include: age, sex, SV1,
SV, and disease_condition.
The random effect is subject variation (ID)
Disease condition is the fixed effect because I am interested in
metabolite differences between those disease conditions.
This command will build a model for each metabolite:
fitMet = fitLmm(fix=c('Sex','Age','SV1,'SV2','disease_condition'),
random='(1|ID)', data=df, start=10)
SV1 and SV2 are surrogate variables (numerical values)
Next I need to calculate the power of my study. Let's say that I have
1,172 individuals total in the study, from which 431 are DR. Let's say
that I would like to determine the power of this study given the
effect size of 0.337.
I know about SIMR software in R but I am not sure how to apply it to
my study design.
I looked at this paper:
https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12504
But I am not sure how to adapt the code given in the tutorial so that
it is matching to mine design.
Can you please help,
Thanks
Ana
______________________________________________ R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Note that such info is available at https://www.r-project.org/mail.html (under "Special Interest Groups"). IMHO the posting guide ought to prominently say something about this. Cheers, Bert
On Fri, Sep 17, 2021 at 1:10 PM Ana Marija <sokovic.anamarija at gmail.com> wrote:
Thank you so much for that info! On Fri, Sep 17, 2021 at 3:06 PM Bert Gunter <bgunter.4567 at gmail.com> wrote:
Wrong list! Post on r-sig-mixed-models, not here. Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Fri, Sep 17, 2021 at 12:22 PM Ana Marija <sokovic.anamarija at gmail.com> wrote:
Hi All,
I plan to identify metabolite levels that differ between individuals
with various retinopathy outcomes (DR or noDR). I plan to model
metabolite levels using linear mixed models ref as implemented in
lmm2met software. The model covariates will include: age, sex, SV1,
SV, and disease_condition.
The random effect is subject variation (ID)
Disease condition is the fixed effect because I am interested in
metabolite differences between those disease conditions.
This command will build a model for each metabolite:
fitMet = fitLmm(fix=c('Sex','Age','SV1,'SV2','disease_condition'),
random='(1|ID)', data=df, start=10)
SV1 and SV2 are surrogate variables (numerical values)
Next I need to calculate the power of my study. Let's say that I have
1,172 individuals total in the study, from which 431 are DR. Let's say
that I would like to determine the power of this study given the
effect size of 0.337.
I know about SIMR software in R but I am not sure how to apply it to
my study design.
I looked at this paper:
https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12504
But I am not sure how to adapt the code given in the tutorial so that
it is matching to mine design.
Can you please help,
Thanks
Ana
______________________________________________ R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.