Direct PD Model

[Generated automatically as a Fitting summary]

Model Description

Name:: direct_pd_pop
Title:: Direct PD Model
Author:: PoPy for PK/PD
Abstract:

A simple direct PD Model, i.e, no delay compartments, based on the amount of drug in the body.
The amount in the central compartment is determined by K, which has been previously estimated for each individual.
The amount in the central compartment influences the rate of removal of a biomarker (KOUT).

Keywords:: pd; one compartment model; direct
Input Script:: direct_pd_pop_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[BASE]	500.0000	781.2044	281.2044	0.5624
f[KOUT]	0.1000	0.0315	0.0685	0.6853

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE]	0.0500	0.0983	0.0483	0.9656
f[ANOISE]	0.2000	0.8209	0.6209	3.1045

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[BASE_isv]	0.0200	0.0339	0.0139	0.6940
f[BASE_isv;KOUT_isv]	0.0000	-0.0050	0.0050	INF
f[KOUT_isv;BASE_isv]	0.0000	-0.0050	0.0050	INF
f[KOUT_isv]	0.0200	0.0024	0.0176	0.8799

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

3917.4035

which required 1.29 iterations and took 193.26 seconds

Fitted f[X] values (after fitting)

f[BASE] = 781.2044
f[KOUT] = 0.0315
f[PNOISE] = 0.0983
f[ANOISE] = 0.8209
f[BASE_isv,KOUT_isv] = [
    [ 0.0339, -0.0050 ],
    [ -0.0050, 0.0024 ],
]

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 ],
]