Body Weight Covariate
[Generated automatically as a Tutorial summary]
Model Description
- Name:
weight_covariate
- Title:
Body Weight Covariate
- Author:
PoPy for PK/PD
- Abstract:
One compartment model with absorption compartment and CL/V parametrisation.
There are no random effects here. Each individual just has a different weight.
The weight is a covariate for the m[CL] clearance parameter for each individual.
Only the f[WT_EFFECT] and f[V] fixed effect parameters are estimated, other f[X] are fixed.
- Keywords:
one compartment model; dep_one_cmp_cl; weight; covariate effect
- Input Script:
- Diagram:
Comparison
True objective value
-483.3718
Final fitted objective value
-486.0798
Compare Main f[X]
Name |
Initial |
Fitted |
True |
Abs. Error |
Prop. Error |
|---|---|---|---|---|---|
f[V] |
15 |
20.3 |
20 |
2.61e-01 |
1.31% |
f[WT_EFFECT] |
1 |
0.666 |
0.75 |
8.43e-02 |
11.25% |
Compare Noise f[X]
No Noise f[X] values to compare.
Compare Variance f[X]
No Variance f[X] values to compare.
Outputs
Fitted f[X] values (after fitting)
f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.2610
f[PNOISE] = 0.1000
f[ANOISE] = 0.0500
f[WT_EFFECT] = 0.6657
Generated data .csv file
- Synthetic Data:
Gen and Fit Summaries
Gen: Body Weight Covariate (gen)
Fit: Body Weight Covariate (fit)
Inputs
True f[X] values (for simulation)
f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.0000
f[PNOISE] = 0.1000
f[ANOISE] = 0.0500
f[WT_EFFECT] = 0.7500
Starting f[X] values (before fitting)
f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 15.0000
f[PNOISE] = 0.1000
f[ANOISE] = 0.0500
f[WT_EFFECT] = 1.0000