:orphan: 





.. _dp_iov_tut:



Disease progression with inter-occasional variance
##################################################

[Generated automatically as a Tutorial summary]

Model Description
*****************


:Name: dp_iov

:Title: Disease progression with inter-occasional variance

:Author: Andrew Cristinacce @ PoPy for PK/PD

:Abstract: 

| Specifies both the pop_gen and pop_fit subscripts.
| Drug concentration defined by one compartment model with first order absorption, using pre-defined PK parameters.
| Individuals are split into four dose groups.
| The concentration in the CENTRAL compartment affects the drug effect, which is an emax model.
| The disease progression model is affected by the concentration. Higher concentrations reduce the disease status
| Inter-occasional variance is included in the LEVEL_PARAMS section and includes 3 occasions.

:Keywords: one compartment model; iov; inter occasional variance; emax; absorption

:Input Script: :download:`dp_iov.pyml <dp_iov.pyml>`

:Diagram: 


.. thumbnail:: compartment_diagram.svg
    :width: 200px


Comparison
**********



True objective value
====================


.. code-block:: pyml

    -2619.7497



Final fitted objective value
============================


.. code-block:: pyml

    -2623.2794



Compare Main f[X]
=================



.. csv-table:: 
    :file: fx_comp_main.csv
    :header-rows: 1


Compare Noise f[X]
==================



.. csv-table:: 
    :file: fx_comp_noise.csv
    :header-rows: 1


Compare Variance f[X]
=====================



.. csv-table:: 
    :file: fx_comp_variance.csv
    :header-rows: 1


Outputs
*******



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


.. code-block:: pyml

    f[BASE] = 386.5315
    f[ALPHA] = 0.0267
    f[EMAX] = 0.4164
    f[EC50] = 2.0606
    f[ANOISE] = 0.1021
    f[BASE_isv,ALPHA_isv,EC50_isv] = [
        [ 0.0768, 0.0512, -0.0094 ],
        [ 0.0512, 0.0647, 0.1058 ],
        [ -0.0094, 0.1058, 0.6191 ],
    ]
    f[BASE_iov,ALPHA_iov] = [
        [ 0.0416, -0.0718 ],
        [ -0.0718, 0.1241 ],
    ]



Generated data .csv file
========================


:Synthetic Data: :download:`synthetic_data.csv <synthetic_data.csv>`


Gen and Fit Summaries
=====================


* Gen: :ref:`dp_iov_gen` (gen)
* Fit: :ref:`dp_iov_fit` (fit)

Inputs
******



True f[X] values (for simulation)
=================================

.. code-block:: pyml

    f[BASE] = 400.0000
    f[ALPHA] = 0.0250
    f[EMAX] = 0.4000
    f[EC50] = 1.8000
    f[ANOISE] = 0.1000
    f[BASE_isv,ALPHA_isv,EC50_isv] = [
        [ 0.1000, 0.0000, 0.0000 ],
        [ 0.0000, 0.0500, 0.0000 ],
        [ 0.0000, 0.0000, 0.9000 ],
    ]
    f[BASE_iov,ALPHA_iov] = [
        [ 0.0300, 0.0000 ],
        [ 0.0000, 0.0200 ],
    ]



Starting f[X] values (before fitting)
=====================================

.. code-block:: pyml

    f[BASE] = 450.0000
    f[ALPHA] = 0.0200
    f[EMAX] = 0.1000
    f[EC50] = 1.0000
    f[ANOISE] = 0.0500
    f[BASE_isv,ALPHA_isv,EC50_isv] = [
        [ 0.2000, 0.0000, 0.0000 ],
        [ 0.0000, 0.1000, 0.0000 ],
        [ 0.0000, 0.0000, 0.5000 ],
    ]
    f[BASE_iov,ALPHA_iov] = [
        [ 0.0200, 0.0000 ],
        [ 0.0000, 0.0100 ],
    ]

