Population Two Compartment Model using VSS transform and Inter-subject Variance

[Generated automatically as a Fitting summary]

Model Description

Name:: iv_two_cmp_vss_isv
Title:: Population Two Compartment Model using VSS transform and Inter-subject Variance
Author:: PoPy for PK/PD
Abstract:

Population One Compartment Model and Inter-subject Variance

Keywords:: two compartment model; iv_two_cmp_vss; proportional noise; additive noise; vss_transform
Input Script:: iv_two_cmp_vss_isv_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[CL]	1.0000	2.0209	1.0209	1.0209
f[V]	15.0000	9.8915	5.1085	0.3406
f[Q]	1.0000	1.0753	0.0753	0.0753
f[VSS]	25.0000	38.3983	13.3983	0.5359

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE]	0.0500	0.0079	0.0421	0.8413
f[ANOISE]	0.0500	0.0098	0.0402	0.8036

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[CL_isv]	0.2000	0.1000	0.1000	0.4999
f[CL_isv;V_isv]	0.0000	0.0390	0.0390	INF
f[CL_isv;Q_isv]	0.0000	0.0006	0.0006	INF
f[CL_isv;VSS_isv]	0.0000	-0.0263	0.0263	INF
f[V_isv;CL_isv]	0.0000	0.0390	0.0390	INF
f[V_isv]	0.1000	0.1686	0.0686	0.6861
f[V_isv;Q_isv]	0.0000	0.0224	0.0224	INF
f[V_isv;VSS_isv]	0.0000	0.0106	0.0106	INF
f[Q_isv;CL_isv]	0.0000	0.0006	0.0006	INF
f[Q_isv;V_isv]	0.0000	0.0224	0.0224	INF
f[Q_isv]	0.0500	0.0526	0.0026	0.0524
f[Q_isv;VSS_isv]	0.0000	0.0084	0.0084	INF
f[VSS_isv;CL_isv]	0.0000	-0.0263	0.0263	INF
f[VSS_isv;V_isv]	0.0000	0.0106	0.0106	INF
f[VSS_isv;Q_isv]	0.0000	0.0084	0.0084	INF
f[VSS_isv]	0.2000	0.3335	0.1335	0.6677

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-1710.7952

which required 1.30 iterations and took 166.55 seconds

Fitted f[X] values (after fitting)

f[CL] = 2.0209
f[V] = 9.8915
f[Q] = 1.0753
f[VSS] = 38.3983
f[PNOISE] = 0.0079
f[ANOISE] = 0.0098
f[CL_isv,V_isv,Q_isv,VSS_isv] = [
    [ 0.1000, 0.0390, 0.0006, -0.0263 ],
    [ 0.0390, 0.1686, 0.0224, 0.0106 ],
    [ 0.0006, 0.0224, 0.0526, 0.0084 ],
    [ -0.0263, 0.0106, 0.0084, 0.3335 ],
]

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[CL] = 1.0000
f[V] = 15.0000
f[Q] = 1.0000
f[VSS] = 25.0000
f[PNOISE] = 0.0500
f[ANOISE] = 0.0500
f[CL_isv,V_isv,Q_isv,VSS_isv] = [
    [ 0.2000, 0.0000, 0.0000, 0.0000 ],
    [ 0.0000, 0.1000, 0.0000, 0.0000 ],
    [ 0.0000, 0.0000, 0.0500, 0.0000 ],
    [ 0.0000, 0.0000, 0.0000, 0.2000 ],
]