Dear nmusers,
I have a question regarding the selection of a model using different estimation
methods. My dataset consists of 20% of the data from a relatively extensive
sampling scheme (about 6 data points per subject, with 1-2 points during the
absorption phase) and the rest from sparse samples (about 3 data points per
subject, with one point near the Tmax). I initially employed the FOCEI
estimation method, but the GOF plots exhibited underestimations in the PRED
values. The pcVPC plots indicated an underestimation of the absorption phase,
where the median observed concentrations were higher than the 95% CI of the
simulated median concentrations.
When I switched the SAEM estimation method for the same structure model, there
was minimal improvement in the GOF plots. However, the pcVPC demonstrated a
better fit, with the median observed concentrations falling within the 95% CI
of the simulated median concentrations.
For the absorption model, I tried a first-order absorption model with lag time
and a transit compartment absorption model. When switching from the FOCEI
algorithm to the SAEM algorithm, both models showed similar trends in the VPC
plots.
Given these observations, would the SAEM estimation method be more appropriate?
What might explain the differences between these two approaches? Additionally,
when using the FOCEI algorithm, MAXEVAL= 0 is set to obtain the empirical Bayes
estimates (EBEs) of individual PK parameters and derive exposure. How should
this be handled when using the SAEM algorithm?
Thanks very much for your explanation.
Best Regards
Qiuyue
Difference between FOCEI and SAEM
I would concentrate on improving the model: FOCEI should be fine (unless you would like to use random effect on the lag time: then may not work well). Some options to improve absorption part are the time-variable KA (implemented using MTIME option) or sequential zero-order then first order absorption.
Thank you
Leonid
Quoted reply history
On 8/16/2024 9:53 AM, Qiuyue Li wrote:
> Dear nmusers,
>
> I have a question regarding the selection of a model using different estimation methods. My dataset consists of 20% of the data from a relatively extensive sampling scheme (about 6 data points per subject, with 1-2 points during the absorption phase) and the rest from sparse samples (about 3 data points per subject, with one point near the Tmax). I initially employed the FOCEI estimation method, but the GOF plots exhibited underestimations in the PRED values. The pcVPC plots indicated an underestimation of the absorption phase, where the median observed concentrations were higher than the 95% CI of the simulated median concentrations. When I switched the SAEM estimation method for the same structure model, there was minimal improvement in the GOF plots. However, the pcVPC demonstrated a better fit, with the median observed concentrations falling within the 95% CI of the simulated median concentrations. For the absorption model, I tried a first-order absorption model with lag time and a transit compartment absorption model. When switching from the FOCEI algorithm to the SAEM algorithm, both models showed similar trends in the VPC plots. Given these observations, would the SAEM estimation method be more appropriate? What might explain the differences between these two approaches? Additionally, when using the FOCEI algorithm, MAXEVAL= 0 is set to obtain the empirical Bayes estimates (EBEs) of individual PK parameters and derive exposure. How should this be handled when using the SAEM algorithm?
>
> Thanks very much for your explanation.
>
> Best Regards
>
> Qiuyue