Dear Nathalie
In answer to your question; yes, it is usual to see this "unstability" in the final few iteration OFVs. When using the IMP method, I often include two sequential $EST commands. The first command will perform optimisation of parameter estimates until a global minimum is found. The second command will then take those parameter estimates and calculate more precise estimates of the objective function value. The second $EST command will have a higher ISAMPLE to reduce the Monte Carlo noise, and have ETYPE=1 (no optimisation of parameter values). I suspect that the number of samples that you are using may not be enough, giving large Monte Carlo noise in the OFV estimate. I suggest that you perform another run with the parameter values set to their final estimates, and with: $EST METHOD=IMP ISAMPLE=10000 INTERACTION LAPLACE NITER=5 SIG=3 PRINT=1 SIGL=6 EONLY=1 NOHABORT RANMETHOD=3S2 The higher number of samples should give a more stable result (although the run time of each iteration will increase significantly). Taking the average OFV of these 5 iterations will give a more accurate estimation of the final OFV. Jon Jon Moss, PhD Modeller BAST Inc Limited Loughborough Innovation Centre Charnwood Wing Holywell Park Ashby Road Loughborough, LE11 3AQ, UK Tel: +44 (0)1509 222908 From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of nathalie.perda...@servier.com Sent: 10 November 2016 07:06 To: nmusers@globomaxnm.com Subject: [NMusers] Estimation method using ITS and IMP iterations Dear NONMEM users, I am building a relatively complex PKPD model (with 47 parameters and 11 differential equations). I had problems using FOCE so I am trying this estimation method : $EST METHOD=ITS INTERACTION LAPLACE NITER=200 SIG=3 PRINT=1 SIGL=6 NOHABORT CTYPE=3 NUMERICAL SLOW $EST METHOD=IMPMAP ISAMPLE=1000 INTERACTION LAPLACE NITER=1000 SIG=3 PRINT=1 SIGL=6 NOHABORT CTYPE=3 IACCEPT=0.4 MAPITER=0 RANMETHOD=3S2 $COV UNCONDITIONAL MATRIX=S TOL=12 SIGL=12 SLOW The iteration for the ITS step seems to be quite stable with some artefacts: iteration 175 OBJ= 4693.4674554341409 iteration 176 OBJ= 4694.2296104065535 iteration 177 OBJ= 4693.7753507970829 iteration 178 OBJ= 4693.9600270372885 iteration 179 OBJ= 4693.5732455834705 iteration 180 OBJ= 4693.6386423202493 iteration 181 OBJ= 4693.6215390721527 iteration 182 OBJ= 4693.6006496138452 iteration 183 OBJ= 4693.7877620448235 iteration 184 OBJ= 4694.1591757809929 iteration 185 OBJ= 4693.2614956897451 iteration 186 OBJ= 4693.5641640401127 iteration 187 OBJ= 4693.5575289919379 iteration 188 OBJ= 4495.6489907149398 iteration 189 OBJ= 4693.7711764252363 iteration 190 OBJ= 4693.6281175153035 iteration 191 OBJ= 4694.1171774559862 iteration 192 OBJ= 4693.7908707845536 iteration 193 OBJ= 4693.7709264605819 iteration 194 OBJ= 4495.9262902940209 iteration 195 OBJ= 4693.3321354894242 iteration 196 OBJ= 4694.3177205227348 iteration 197 OBJ= 4694.1301486616576 iteration 198 OBJ= 4694.2898587322170 iteration 199 OBJ= 4693.8304358341920 iteration 200 OBJ= 4691.6818293505230 #TERM: OPTIMIZATION WAS NOT COMPLETED The IMP step seems less stable : iteration 120 OBJ= 4314.8310660241377 eff.= 446. Smpl.= 1000. Fit.= 0.96389 iteration 121 OBJ= 4326.9079856676717 eff.= 448. Smpl.= 1000. Fit.= 0.96409 iteration 122 OBJ= 4164.6649529423103 eff.= 479. Smpl.= 1000. Fit.= 0.96392 iteration 123 OBJ= 4299.9887619753636 eff.= 432. Smpl.= 1000. Fit.= 0.96395 iteration 124 OBJ= 4303.9571213327054 eff.= 399. Smpl.= 1000. Fit.= 0.96349 iteration 125 OBJ= 4328.9835950930074 eff.= 417. Smpl.= 1000. Fit.= 0.96423 iteration 126 OBJ= 4304.3861595488252 eff.= 550. Smpl.= 1000. Fit.= 0.96392 iteration 127 OBJ= 4291.0862736663648 eff.= 422. Smpl.= 1000. Fit.= 0.96430 iteration 128 OBJ= 4326.2378678645500 eff.= 407. Smpl.= 1000. Fit.= 0.96409 iteration 129 OBJ= 4157.5352046539456 eff.= 406. Smpl.= 1000. Fit.= 0.96404 iteration 130 OBJ= 4332.6894073732456 eff.= 399. Smpl.= 1000. Fit.= 0.96399 iteration 131 OBJ= 4357.5343346793761 eff.= 493. Smpl.= 1000. Fit.= 0.96414 Convergence achieved iteration 131 OBJ= 4336.1893012015007 eff.= 417. Smpl.= 1000. Fit.= 0.96369 #TERM: OPTIMIZATION WAS COMPLETED I think the ITS step is OK with an objective function ~ 4690. The "unstability" of the IMP step is it usual ? Nonmem is completed at the end.. I want to trust in this model, but am I right ? Thanks in advance for your answers. Nathalie
