# 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 | 2.0018 | 0.9982 | 0.3327
f[INT] | 16.0000 | 7.8714 | 8.1286 | 0.5080
f[KOUT] | 0.1000 | 0.0500 | 0.0500 | 0.4998

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[ANOISE] | 5.0000 | 3.0759 | 1.9241 | 0.3848

### Compare Variance f[X]

### Population simulated (sim) plots

| indOBS_vs_TIME

## Outputs

### Final objective value

    811.9569

which required 1.26 iterations and took 12.17 seconds

### Fitted f[X] values (after fitting)

    f[AMP] = 2.0018
    f[INT] = 7.8714
    f[KOUT] = 0.0500
    f[ANOISE] = 3.0759

### 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