Cosine circadian model

[Generated automatically as a Fitting summary]

Model Description

Name:

circ_cos

Title:

Cosine circadian model

Author:

PoPy for PK/PD

Abstract:

A PD Model based on the concentration of drug in the body.

The PD model uses a cosine 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 parameters, which have been estimated for each individual.

The concentration in the central compartment influences the rate of removal of a biomarker (KOUT).

Keywords:

PD; Pharmacodynamics; cosine function; Circadian rhythm

Input Script:

circ_cos_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[AMP]	3.0000	2.0077	0.9923	0.3308
f[INT]	16.0000	20.0368	4.0368	0.2523
f[KOUT]	0.1000	0.0502	0.0498	0.4985

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ANOISE]	5.0000	3.0888	1.9112	0.3822

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

813.7698

which required 1.19 iterations and took 11.45 seconds

Fitted f[X] values (after fitting)

f[AMP] = 2.0077
f[INT] = 20.0368
f[KOUT] = 0.0502
f[ANOISE] = 3.0888

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