Re: ETAs & SIGMA in external validation

Dear Tingjie, If I understand your description correctly, you would like to evaluate the published model (and point estimates of population parameters) using GoF plots (residual-error and eta-plots), rather than via simulation (e.g. VPC or PPC)? At least for the latter it would be necessary to constrain individual parameters from (zero and) negative space (for parameters which must be positive). The solution you initially implemented will bias the parameter distribution severely, since only values greater than or equal to the typical parameter value is allowed. For estimation you can add NOABORT to the $ESTIMATION line. Right after the individual parameter has been assigned its value you can check that it is positive: PARA = TVPARA * (1+ETA(1)) IF (PARA.LE.0) EXIT 1 23 In simulation mode, you can instead draw a new eta in subjects that have a negative parameter value. I have written some code for you below, but please check for any typos :>) Also, notice this is an example which avoids negative parameter values for a single parameter, but you can implement the same solution with multiple individual parameters in the DO WHILE block. Also, before you go ahead and try to fix anything related to etas in estimation: Check that the code and data you have put in place is reasonable. The first subject that fails with a negative parameter value: Can you find anything particular in your dataset for this individual? For example, you may have included zero DV values in your data set, or you may have coded a missing covariate as -99. Finally, the residual error is usually much larger than the assay error, inflated by e.g. adherence and errors in sample collection, imperfect model, etc. Many things may change from one study to the next. A well controlled study (or in some cases a better assay) could result in lower residual error. More commonly, changes in population or inclusion criteria may change IIV in parameters (as well as typical or population values). However, as a starting point for your external evaluation, it may be good to assume that all population parameters are the same as in the published model, both fixed and random effects. Best wishes Jakob TVPARA = THETA(1) PARA = TVPARA * (1+ETA(1)) ;Sampling etas until the new subject has Para>0 IF(ICALL.EQ.4.AND.NEWIND.NE.2) THEN DO WHILE (PARA.LE.0) CALL SIMETA(ETA) PARA = TVPARA * (1+ETA(1)) ENDDO ENDIF ;Etas that do not need resampling should be declared after the above DO WHILE block. They should follow below […] $SIMULATION (123456 NEW) (7891011 UNIFORM) ONLYSIMULATION […] ; The SIMETA requires an additional seed number, see nmhelp for more info Jakob Ribbing, Ph.D. Senior Consultant, Pharmetheus AB Cell/Mobile: +46 (0)70 514 33 77 [email protected] www.pharmetheus.com http://www.pharmetheus.com/ Phone, Office: +46 (0)18 513 328 Uppsala Science Park, Dag Hammarskjölds väg 52B SE-752 37 Uppsala, Sweden >