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Direct PD Model

[Generated automatically as a Fitting summary]

Model Description

Name:

direct_pd

Title:

Direct PD Model

Author:

PoPy for PK/PD

Abstract:

A simple direct PD Model, 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_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[BASE]

500.0000

800.0499

300.0499

0.6001

f[KOUT]

0.1000

0.0300

0.0700

0.6999

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[ANOISE]

5.0000

9.9922

4.9922

0.9984

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

460.5766

which required 1.30 iterations and took 4.52 seconds

Fitted f[X] values (after fitting)

f[BASE] = 800.0499
f[KOUT] = 0.0300
f[ANOISE] = 9.9922

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[ANOISE] = 5.0000
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