Dear All
Another solution would be to first generate (or add) the WT, GENDER and
Genotype and put these in the indata set, with R most of what you are doing in
NONMEM would be just 2-3 lines of code.
I you want to peruse this I could guide you on how to.
Also, a comment on the NONMEM code below,
Dear all, an alternative which I try to use on strictly positive parameters is
to estimate on log-scale. Then I think often the assymptitic approximation is
more true and the resulting measures of parameter uncertainty are more reliable.
BW
Magnus
Från:
Dear Nele, here are some thoughts:
The idea with the MCPmod is twofold,
a) provide a procedure for testing for a treatment effect and in that test
incorporate all doses studies and still maintain control of type I error.
b) If significance in a) continue with framework for estimating the dose
Hi Matts, I agree on your conclusions and think the issue of missing data is a
very similar problem. There the missing completely at random and missing at
random would match your examples a and b. For missing data there exists
litterature and also perhaps a better understanding among
Hi
To add on Martin’s suggestion, one item to think of when using theta for
estimating the variances of eta is to use log scaled STD as your model
parameter, so instead of THETA*ETA (with OMEGA fixed) use exp(THETA)*ETA.
This way you would assume a log-normal prior for the standard deviation of
Hi Penny,
I suspect you are right in your conclusion that number of records for each id
matters in this case.
My solution to your problem would be to add columns for IIV to your dataset
outside of NONMEM.
Quite easily done in R.
Use the rmvnorm R function to simulate your etas, 600 rows times
