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

[Daniel Rubi]

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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