From: HUI, Ka Ho <*matthew.hui*>

Date: Mon, 9 Nov 2015 14:43:21 +0000

Dear all,

I have some population PK data which are in general very sparse (95% have o=

nly 1 blood sample between 2 successive doses). I developed a population PK=

model with the one-compartment model with 1st order absorption. The progre=

ss is generally okay except that whenever a random effect, i.e. *(1+ETA(1))=

, is used to describe distribution of Vd, OMEGA would be estimated to be ve=

ry large (around 45% in terms of CV, with 80% Shrinkage), despite statistic=

al significance (dOF approx. -5.5). So I dropped the random effect and expr=

essed Vd in terms of a single fixed effect. When the final model has come o=

ut, I performed bootstrap and found that most estimates are accurate except=

Vd, which has a very large standard error and bias (mean 232, bias 49, SE =

156), while the estimates for CL and other parameters look normal. I then c=

onstructed the predictive plots for the developed model using both the orig=

inal estimates (i.e. estimates using my original dataset) (#1) and estimate=

s from one of the bootstrap runs which has an extreme estimate of Vd (9xx) =

(#2), and found out that the two plots of plasma profiles are quite differe=

nt in terms of the shape (#1 is "taller", #2 is much flatter) but have simi=

lar average Cp.

These seem to be suggesting that given my sparse data, it is impossible to =

require accurate estimations of both CL and Vd. Apart from fixing Vd to a f=

ixed value, is there any other possible solutions? Or is there anything tha=

t I might have overlooked?

Thanks and regards,

Matthew

Received on Mon Nov 09 2015 - 09:43:21 EST

Date: Mon, 9 Nov 2015 14:43:21 +0000

Dear all,

I have some population PK data which are in general very sparse (95% have o=

nly 1 blood sample between 2 successive doses). I developed a population PK=

model with the one-compartment model with 1st order absorption. The progre=

ss is generally okay except that whenever a random effect, i.e. *(1+ETA(1))=

, is used to describe distribution of Vd, OMEGA would be estimated to be ve=

ry large (around 45% in terms of CV, with 80% Shrinkage), despite statistic=

al significance (dOF approx. -5.5). So I dropped the random effect and expr=

essed Vd in terms of a single fixed effect. When the final model has come o=

ut, I performed bootstrap and found that most estimates are accurate except=

Vd, which has a very large standard error and bias (mean 232, bias 49, SE =

156), while the estimates for CL and other parameters look normal. I then c=

onstructed the predictive plots for the developed model using both the orig=

inal estimates (i.e. estimates using my original dataset) (#1) and estimate=

s from one of the bootstrap runs which has an extreme estimate of Vd (9xx) =

(#2), and found out that the two plots of plasma profiles are quite differe=

nt in terms of the shape (#1 is "taller", #2 is much flatter) but have simi=

lar average Cp.

These seem to be suggesting that given my sparse data, it is impossible to =

require accurate estimations of both CL and Vd. Apart from fixing Vd to a f=

ixed value, is there any other possible solutions? Or is there anything tha=

t I might have overlooked?

Thanks and regards,

Matthew

Received on Mon Nov 09 2015 - 09:43:21 EST