[R-sig-ME] A model for repeated treatments and repeated outcomes

Peter Claussen dakotajudo at mac.com
Fri Feb 12 18:43:42 CET 2016


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.


> 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
>         [[alternative HTML version deleted]]
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