[R-sig-ME] A model for repeated treatments and repeated outcomes
Daniel Rubi
daniel_rubi at ymail.com
Fri Feb 12 20:16:01 CET 2016
Thanks a lot for the advice Peter.
Just to make sure I got it correctly. Will the model would be this multivariate model:
gfrs_post_op ~ gfr_pre_op1 + gfr_pre_op2 + gfr_pre_op3 + SamplePeriod + (SamplePeriod | Patient)
where gfrs_post-op is a matrix of patient x gfr_post_op
Thanks a lot,
Dan
--------------------------------------------
On Fri, 2/12/16, Peter Claussen <dakotajudo at mac.com> wrote:
Subject: Re: [R-sig-ME] A model for repeated treatments and repeated outcomes
To: "Daniel Rubi" <daniel_rubi at ymail.com>
Cc: "r-sig-mixed-models at r-project.org" <r-sig-mixed-models at r-project.org>
Date: Friday, February 12, 2016, 12:43 PM
Dan,
If I may jump in.
If I read this correctly, you have two random
samples per patient - a random sample of pre-operation GRF
and a random sample post-operation. I think these should be
considered random since you state the points are not evenly
spaced in either case.
So
you add a factor for sampling period (SamplePeriod =
c(“Pre”,”Post”)) and model random effect as
(SamplePeriod | Patient).
That’s my first thought - I’m not sure
given three points per sample period you can reasonably
measure trends.
Peter
> On Feb 11, 2016, at 7:01
PM, Daniel Rubi via R-sig-mixed-models <r-sig-mixed-models at r-project.org>
wrote:
>
> Dear
Thierry,
>
> Thanks a
lot for the help.
>
>
Please allow me to explain better.
>
> What I'm trying to estimate is whether
the trend in pre-operation GFR affects the levels of
post-operation GFR.
> The GFR is a
measurement that assesses kidney function based on a blood
sample and for that reason it reflects the functions of both
kidneys.
> The operation is either
partial or total removal of one kidney - that is affected.
Therefore it is supposed to prevent GFR from escalating.
>
> What I'm
interesting in estimating is whether the trend of
pre-operation GFR (along the time pre-operation time points)
is a good predictor of the post-operatoin GFR. It's not
clear at which post-operation time point it is correct to
measure GFR and therefore I have 3 time points.
>
> The trivial thing to
do is to estimate the slope of pre-operation GFR and
individually regress each of the post-operation GFRs against
thos.
>
> But I'm
wondering whether there's anything better?
>
> Thanks a lot,
> Dan
>
>
>
--------------------------------------------
> On Wed, 2/10/16, Thierry Onkelinx <thierry.onkelinx at inbo.be>
wrote:
>
> Subject:
Re: [R-sig-ME] A model for repeated treatments and repeated
outcomes
> To: "Daniel Rubi"
<daniel_rubi at ymail.com>
> Cc: "r-sig-mixed-models at r-project.org"
<r-sig-mixed-models at r-project.org>
> Date: Wednesday, February 10, 2016, 3:54
AM
>
> Dear
> Dan,
> You'll need
to
> provide more information. What is
the global pattern that
> you expect
(linear? quadratic? non-linear?) How to you thing
> that the operation can effect the GFR? You
need to answer
> those kind of questions
so that you can make a sensible
> fixed
effects part of the model.
> The random
effect is probably just
> ~1|Patient. And
a corCAR1(form = ~ Time) can handle the
>
temporal correlation within the patient.
> Best regards,
> ir.
Thierry Onkelinx
> Instituut voor natuur-
en bosonderzoek /
> Research Institute
for Nature and Forest
> team Biometrie
& Kwaliteitszorg / team
> Biometrics
& Quality Assurance
> Kliniekstraat
25
> 1070
>
Anderlecht
> Belgium
>
> To call in the statistician after
the
> experiment is done may be no more
than asking him to perform
> a
post-mortem examination: he may be able to say what the
> experiment died of. ~ Sir Ronald Aylmer
Fisher
> The plural of anecdote is not
data. ~ Roger
> Brinner
> The combination of some data and an
> aching desire for an answer does not
ensure that a
> reasonable answer can be
extracted from a given body of
> data. ~
John Tukey
>
>
2016-02-09 0:55 GMT+01:00
> Daniel Rubi
via R-sig-mixed-models <r-sig-mixed-models at r-project.org>:
> I have the following experimental
> design:Measurements of kidney function (in
units called GFR)
> taken at several time
points pre-operation (time points not
>
evenly spaced) and at several time points post-operation
> (neither evenly spaced).
>
> Here's an
example of my data in R code:
>
> set.seed(1)df <- data.frame(patient =
letters[1:10],
>
gfr_ten_days_prop = rnorm(10,5,1),
> gfr_five_days_prop = rnorm(10,10,1),
gfr_three_days_prop =
> rnorm(10,12,1),
gfr_one_day_postop =
> rnorm(10,10,1), gfr_one_day_postop =
rnorm(10,5,1),
> gfr_one_day_postop =
rnorm(10,2,1))
>
>
>
> I'm looking
for a model which will estimate the effect
> of pre-operation GFR on post-operation
GFR, taking into
> account the different
times at which GFRs were measured pre-
>
and post-operation.One additional possible caveat - my
data
> contain missing values (NAs).
>
> I'm having a
hard time seeing how a mixed-effects model
> fits this problem since in all the
examples of repeated
>
measures/longitudinal data I came across in each time
point
> the response is measured whereas
here it is more a
> predictive question -
how strong does each pre-operation GFR
>
predict pos-operation GFR, where the time at which GFRs
were
> measured may matter.
>
> Thanks a lot,Dan
>
>
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>
>
>
>
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