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Sine circadian model

[Generated automatically as a Fitting summary]

Model Description

Name:circ_sin
Title:Sine circadian model
Author:PoPy for PK/PD
Abstract:
A PD Model based on the amount of drug in the body.
The PD model uses a sine function which simulates a circadian rhythm for the generation of a biomarker.
The amount in the central compartment is determined by CL and V, PK patameters , which have been previosly estimated for each individual.
The amount in the central compartment influences the rate of production of a biomarker.
Keywords:PD; Pharmacodynamics; sine function; Circadian rhythm
Input Script:circ_sin_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[AMP] 3.0000 2.0021 0.9979 0.3326
f[INT] 16.0000 7.8694 8.1306 0.5082
f[KOUT] 0.1000 0.0500 0.0500 0.5001

Compare Noise f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[ANOISE] 5.0000 3.0706 1.9294 0.3859

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

811.8798

which required 1.26 iterations and took 16.87 seconds

Fitted f[X] values (after fitting)

f[AMP] = 2.0021
f[INT] = 7.8694
f[KOUT] = 0.0500
f[ANOISE] = 3.0706

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[AMP] = 3.0000
f[INT] = 16.0000
f[KOUT] = 0.1000
f[ANOISE] = 5.0000
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