Indirect_PKPD_model

[Generated automatically as a Fitting summary]

Model Description

Name:: indirect_pd_pop
Title:: Indirect_PKPD_model
Author:: PoPy for PK/PD
Abstract:

A indirect (i.e. uses delay compartments) PD Model, based on the amount of drug in the body, delayed by two lag compartments
The amount in the central compartment is determined by K, which has been estimated for each individual.
The amount in the central compartment influences the rate of removal of a biomarker (KOUT).

Keywords:: pd; one compartment model; indirect; delay compartment
Input Script:: indirect_pd_pop_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[BASE]	500.0000	781.9491	281.9491	0.5639
f[KOUT]	0.1000	0.0286	0.0714	0.7136

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE]	0.0500	0.0967	0.0467	0.9335
f[ANOISE]	0.2000	0.2009	0.0009	0.0047

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[BASE_isv]	0.0200	0.0345	0.0145	0.7262
f[BASE_isv;KOUT_isv]	0.0000	0.0047	0.0047	INF
f[KOUT_isv;BASE_isv]	0.0000	0.0047	0.0047	INF
f[KOUT_isv]	0.0200	0.0407	0.0207	1.0348

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

3933.3780

which required 1.30 iterations and took 163.29 seconds

Fitted f[X] values (after fitting)

f[BASE] = 781.9491
f[KOUT] = 0.0286
f[PNOISE] = 0.0967
f[ANOISE] = 0.2009
f[BASE_isv,KOUT_isv] = [
    [ 0.0345, 0.0047 ],
    [ 0.0047, 0.0407 ],
]

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[BASE] = 500.0000
f[KOUT] = 0.1000
f[PNOISE] = 0.0500
f[ANOISE] = 0.2000
f[BASE_isv,KOUT_isv] = [
    [ 0.0200, 0.0000 ],
    [ 0.0000, 0.0200 ],
]