Re: Weighting observations

Hi Robert, I have not seen it done, but you can try to enforce the weighting by playing with the error model. For example, if you have 2 DVs, PK and PD, run the model without any weighting and with the proportional error for both variables: $ERROR SDPK=THETA(1) SDPD=THETA(2) IF(PK) Y=DVPK+SDPK*EPS(1) IF(PD) Y=DVPD+SDPD*EPS(2) $SIGMA 1 FIXED 1 FIXED and got THETA(1)=0.1 and THETA(2)=0.2 you may try SDPK=THETA(1) SDPD=4*THETA(1) effectively forcing the model to downgrade the PD observations. Yet another possibility is to fit that main variable first, fix the parameters related to that variable (population or even population and individual), and then add data for the second variable. Or may be you can estimate the model with only the first variable, and then fix the residual error on the estimated value: this would also force the model to maintain the quality of the first-variable fit. More mild control is to fit that main variable first and then add priors to the model, more informative for the parameters that describe the first variable and uninformative for the second one. If you implement any of these ideas, please let the group know how it worked (or not). This discussion is somewhat similar to the question whether to perform sequential or simultaneous fit of the PK and PD data, so you may look on the references, posters, discussion topics related to this question for additional ideas. Thanks Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566