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

[Thierry Onkelinx]

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 op 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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