SEED status with SAEM method

Hello nmusers, I've tried to test the SAEM method on my dataset (1 sample for each ID) with different SEED status. Sometimes, I've got good results (with RSE <50%), but sometimes I can't get it. Example: $EST METHOD=SAEM INTERACTION NBURN=3000 NITER=500 AUTO=1 PRINT=100 SEED=123456789 $EST METHOD=IMP EONLY=1 PRINT=1 NITER=5 ISAMPLE=1000 MAPITER=0 I've got EPS : 0.00064 (RSE=63%) but $EST METHOD=SAEM INTERACTION NBURN=3000 NITER=500 AUTO=1 PRINT=100 SEED=123235489 $EST METHOD=IMP EONLY=1 PRINT=1 NITER=5 ISAMPLE=1000 MAPITER=0 I've got EPS : 0.0223 (RSE=18%) Could i trust on the good results (EPS=0.0223)? How can we control this problem? Thank you very much, Hai, LE Ba Ph.D Student La faculté de Pharmacie - Aix Marseille université Hanoi university of Pharmacy Email: [email protected] [email protected] <[email protected]>; [email protected]

Dear Le Ba, The most optimal way would be to tune other SAEM settings to see whether the results become more insensitive to the seed. Your use of a lower NBURN might be because you do not reach convergence. If that is indeed the case, that would also an objective to reach by improving settings. There are many settings to tune, please refer to the nonmem guide (nm7XX.pdf) for a list of options. Hope this helps, Jeroen http://pd-value.com jeroen +31 6 23118438 -- More value out of your data!

Dear Le Ba, The most optimal way would be to tune other SAEM settings to see whether the results become more insensitive to the seed. Your use of a lower NBURN might be because you do not reach convergence. If that is indeed the case, that would also an objective to reach by improving settings. There are many settings to tune, please refer to the nonmem guide (nm7XX.pdf) for a list of options. Hope this helps, Jeroen http://pd-value.com [email protected] @PD_value +31 6 23118438 -- More value out of your data!

Dear LE Ba, You did not share much information on your model, except that you have one observations per subject and that EPS (or rather sigma) is estimated (I assume then a continuous endpoint). Is thisyour only level of random effect, so that you have no omega/eta for IOV or IIV? With one observations per subject it will be difficult to find support for more than one level. And for a model with only residual error (no IIV), would not the FO Estimation method be sufficient, so why would you need to use SAEM for this data/model? Best regards Jakob Jakob Ribbing, Ph.D. Senior Consultant, Pharmetheus AB Cell/Mobile: +46 (0)70 514 33 77 [email protected] www.pharmetheus.com Phone, Office: +46 (0)18 513 328 Uppsala Science Park, Dag Hammarskjölds väg 36B SE-752 37 Uppsala, Sweden This communication is confidential and is only intended for the use of the individual or entity to which it is directed. It may contain information that is privileged and exempt from disclosure under applicable law. If you are not the intended recipient please notify us immediately. Please do not copy it or disclose its contents to any other person.