Dear NONMEM users,
1) I am fitting Studies-reported means of a parameter X measured in human subjects as a function of TIME. The studies report the mean plus/minus SD of that parameter rather than the actual subject measurements. 2) A snapshot of the main columns in my nmdata is provided below. The "ID" column represent the STUDY number, the "DV" column is the reported mean of parameter X at TIME=t, the "SD" is the standard deviation of the observations in the subjects at TIME=t. 3) I am using $PRED for fitting. 4) I am using one exponential decline function to fit the means~TIME. I am weighting for the number of subjects in each study in the error model. A = THETA(1)*EXP(ETA(1)) ;ETA1 is between STUDY variability on A ALPHA = THETA(2)*EXP(ETA(2)) ;ETA2 is between STUDY variability on ALPHA IPRED = (A)*exp(-ALPHA*TIME) Y = IPRED *(1+EPS(1)/SQRT(NSUB)) My Question: 5) Is there any way where I can incorporate the SDs that I have to inform about the between SUBJECT variability in the model fitting? I am able to get a very good model as described above; however, I haven't included the SDs in anyway in the model fitting. I only accounted for the number of subjects in the error model. I am not sure if there is a way to account for SDs either in the error model or if there is a way to incorporate them to the DVs? I would appreciate any thoughts on this. Thank you. ID NSUB TIME DV SD 1 10 0.083333 4.776667 0.230317 1 10 0.5 3.713333 0.355235 1 10 0.583333 3.556667 0.361091 1 10 0.75 3.2 0.339621 1 10 1.083333 2.816667 . 1 10 1.333333 2.613333 0.304487 1 10 1.416667 2.823333 0.290825 1 10 2 2.23 0.202992 2 5 0.5 6.36 0.329154 2 5 1 6 . 2 5 1.5 5.76 0.821635 2 5 2 5.18 0.973441 2 5 2.5 4.76 1.347797 2 5 3 4.13 1.680903 2 5 3.5 3.17 1.618905 Sincerely, Ahmad Abuhelwa Adelaide, South Australia Australia Email: [email protected]<mailto:[email protected]>
