simulation with uncertainty of THETA

Dear all, When do the simulation, if I have all of the parameter information from literature and internal data, I will fix the PKPD parameter and add about 30% for PK and 40% for PD as IIV. But my question is do we need to think about the CV% of THETA, since we fixed it, we could not add it in one run. if it is necessary, for instance, we will add 20% of CV to one mean THETA, how to do it, or does PsN can implement this issue? Best Ying

Ying, I think its a reasonable starting point to add 30-40% BSV to PKPD parameters but for simulation you should also think carefully about adding an off-diagonal covariance to reflect the very common correlation between random BSV. The subject of your email suggests you are using CV% to refer to the uncertainty of THETA. If you a really interested in simulating uncertainty as well as variability (BSV) then you should be thinking about all the parameters (THETA, OMEGA, SIGMA). Once again if you add uncertainty you should be thinking about the correlation between the parameters. Can you explain more clearly what the objective of your simulation might be? Best wishes, Nick

Hi Ying, You mention that you have internal (individual level?) data but also literature data to complement this information. One way to combine the two in nonmem is via the $PRIOR functionality. If your literature source includes uncertainty of the estimated parameters it would be far less arbitrary to include this as a prior and that way you may not have to fix parameters from literature. The nonmem covariance matrix would then contain uncertainty and correlation between the estimates for all population parameters. Are you saying that your internal data does not contain enough information to estimate IIV on any PK or PD parameter? With regards to PsN: Simulations with uncertainty in population parameters can be incorporated according to three different approaches. These are available for the PsN programs vpc/npc and sse (see PsN documentation). If you decide to use fixed parameters and to arbitrarily add some uncertainty: You will need to create a parameter table similar to the bootstrap raw_results file and plug that into the subsequent simulation. In the normal case bootstrap followed by vpc or sse is dead easy to perform in PsN (and the bootstrap would create that table for you to plug into simulations). In your case you may find that the approach with $PRIOR is more efficient (and somewhat less arbitrary). Finally, just a caution to seeing the THETA as representing the mean. Often random inter-individual variability (IIV) is added as a log normal distribution around the typical value (defined by a single theta, assuming no covariates in model). The typical value in this case is the median, but not the mean. For a single parameter, IIV of 40%-approx. CV translates into mean only 8% above the median, whereas IIV of 80%-approx CV translates into mean 38% higher than the median (i.e. mean parameter value is 38% higher than the theta). Sometimes these biases stack up, so treating the mean curve from literature as the PRED curve in the individual model may heavily bias your results. Likewise, if IIV is high, literature values based on a naive pooled approach (representing a mean curve) would be far away from the typical values in the population model. Depending on what information you are combining (from aggregate data and individual "patient" data) and how thorough you want to be there is a range of different approaches for dealing with this. Best wishes Jakob