Depot + One compartment PK with BLQ

[Generated automatically as a Tutorial summary]

Model Description

Name:: blq_pk
Title:: Depot + One compartment PK with BLQ
Author:: PoPy for PK/PD
Abstract:

Depot One Comp PK model, with BLQ (below level of quantification) observations.

Keywords:: tutorial; pk; advan4; dep_two_cmp; blq
Input Script:: blq_pk_tut.pyml
Diagram:

Comparison

True objective value

-763.2175

Final fitted objective value

-767.6650

Compare Main f[X]

Name	Initial	Fitted	True	Abs. Error	Prop. Error
f[KA]	1	0.199	0.2	9.47e-04	0.47%
f[CL]	1	1.95	2	5.25e-02	2.62%
f[V1]	20	48.7	50	1.28e+00	2.56%

Compare Noise f[X]

Name	Initial	Fitted	True	Abs. Error	Prop. Error
f[PNOISE]	0.1	0.149	0.15	1.12e-03	0.75%

Compare Variance f[X]

Name	Initial	Fitted	True	Abs. Error	Prop. Error
f[KA_isv]	0.05	0.0681	0.1	3.19e-02	31.93%
f[KA_isv;CL_isv]	0.01	0.033	0.02	1.30e-02	64.95%
f[KA_isv;V1_isv]	0.01	-0.000844	0.01	1.08e-02	108.44%
f[CL_isv;KA_isv]	0.01	0.033	0.02	1.30e-02	64.95%
f[CL_isv]	0.05	0.0385	0.03	8.55e-03	28.49%
f[CL_isv;V1_isv]	0.01	0.031	0.02	1.10e-02	55.14%
f[V1_isv;KA_isv]	0.01	-0.000844	0.01	1.08e-02	108.44%
f[V1_isv;CL_isv]	0.01	0.031	0.02	1.10e-02	55.14%
f[V1_isv]	0.05	0.103	0.09	1.29e-02	14.34%

Outputs

Fitted f[X] values (after fitting)

f[KA] = 0.1991
f[CL] = 1.9475
f[V1] = 48.7184
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0681, 0.0330, -0.0008 ],
    [ 0.0330, 0.0385, 0.0310 ],
    [ -0.0008, 0.0310, 0.1029 ],
]
f[PNOISE] = 0.1489
f[ANOISE] = 0.0100

Generated data .csv file

Synthetic Data:: synthetic_data.csv

Gen and Fit Summaries

Gen: Depot + One compartment PK with BLQ (gen)
Fit: Depot + One compartment PK with BLQ (fit)

Inputs

True f[X] values (for simulation)

f[KA] = 0.2000
f[CL] = 2.0000
f[V1] = 50.0000
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.1000, 0.0200, 0.0100 ],
    [ 0.0200, 0.0300, 0.0200 ],
    [ 0.0100, 0.0200, 0.0900 ],
]
f[PNOISE] = 0.1500
f[ANOISE] = 0.0100

Starting f[X] values (before fitting)

f[KA] = 1.0000
f[CL] = 1.0000
f[V1] = 20.0000
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0500, 0.0100, 0.0100 ],
    [ 0.0100, 0.0500, 0.0100 ],
    [ 0.0100, 0.0100, 0.0500 ],
]
f[PNOISE] = 0.1000
f[ANOISE] = 0.0100