Depot + One compartment PK with BLQ

[Generated automatically as a Fitting 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_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KA]	1.0000	0.1991	0.8009	0.8009
f[CL]	1.0000	1.9475	0.9475	0.9475
f[V1]	20.0000	48.7184	28.7184	1.4359

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE]	0.1000	0.1489	0.0489	0.4888

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KA_isv]	0.0500	0.0681	0.0181	0.3615
f[KA_isv;CL_isv]	0.0100	0.0330	0.0230	2.2991
f[KA_isv;V1_isv]	0.0100	-0.0008	0.0108	1.0844
f[CL_isv;KA_isv]	0.0100	0.0330	0.0230	2.2991
f[CL_isv]	0.0500	0.0385	0.0115	0.2291
f[CL_isv;V1_isv]	0.0100	0.0310	0.0210	2.1029
f[V1_isv;KA_isv]	0.0100	-0.0008	0.0108	1.0844
f[V1_isv;CL_isv]	0.0100	0.0310	0.0210	2.1029
f[V1_isv]	0.0500	0.1029	0.0529	1.0581

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-767.6650

which required 1.13 iterations and took 135.24 seconds

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

Fitted parameter .csv files

Fixed Effects:: fx_params.csv (fit)
Random Effects:: rx_params.csv (fit)
Model params:: mx_params.csv (fit)
State values:: sx_params.csv (fit)
Predictions:: px_params.csv (fit)
Likelihoods:: lx_params.csv (fit)

Inputs

Input Data:: cx_obs_params.csv

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