[R-sig-ME] A model for repeated treatments and repeated outcomes
daniel_rubi at ymail.com
Fri Feb 12 02:01:44 CET 2016
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,
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
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.
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek /
Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team
Biometrics & Quality Assurance
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
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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