Weights for AUC in a logistics regre

From pg@a340.spc.univ-lyon1.fr Fri Oct 18 06:35:45 1996 Subject: Weights for AUC in a logistics regre Hi Farkad and Mats, Just to answer the following specific point raised by Mats after Farkad Email: > An alternative may be to do a simultaneous analysis of the PK and the > PD data. NONMEM supports logistic regression, but I don't know if you > can simultaneously fit continuous PK data. If this doesn't work there is To my knowledge, without any additional FORTRAN coding you cannot right now do a simultaneous analysis of continuous PK and categorical PD data. The reason for this is that either your PRED routine computes a prediction for your continuous DV based on the model and parameter values, or it computes a probability (e.g. P(Y=1 | THETA, OMEGA) with Y= 1 for yes, Y=0 for no). In the first case the prediction is passed to NONMEM which computes the default contribution of the current observation to the (conditional) objective function. In the second case (as written in NONMEM help for CCONTR): "A user-supplied CCONTR subroutine is used to compute the (non-default) contribution to the conditional objective function from a level 2 record. It is used to override the NONMEM default. CCONTR may be used only when a user-supplied CONTR routine is used. CCONTR is required when there are no epsilons or etas and in other situations, e.g., with categorical population data." The reason for this is that in the case of a logistic regression, the probability that you compute is directly a piece of your likelihood, and as you know, NONMEM objective function is nothing else than -twice the log likelihood. With NONMEM version IV you have to write your own CONTR.f and CCONTR.f in the case of logistic regression. This will be automatically implemented within next version V using option F=LIKE in $ESTIM, as presented by Stuart at the last PAGE. However, to go back to the initial question, I don't see any theoretical reason for not being able to compute simultaneously the likelihood for continuous and categorical data. The question is how to implement it in NONMEM? I'm pretty sure that we can imagine, based on an indicator variable, some coding to tell NONMEM wether the F is a prediction computed by some ADVAN (for PK data) or a probability computed in ERROR subroutine, with alternative call to the correct CONTR annd CCONTR functions. Stuart would surely be of great help for doing so. A suivre, ... Pascal Girard Service de Pharmacologie Clinique Hospices Civils de Lyon 162 av Lacassagne 69003 Lyon FRANCE