:orphan: 





.. _dp_compartmental_tut:



linear_disease_progression_compartmental_model
##############################################

[Generated automatically as a Tutorial summary]

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


:Name: dp_compartmental

:Title: linear_disease_progression_compartmental_model

:Author: Andrew Cristinacce @ PoPy for PK/PD

:Abstract: 

| Specifies both the pop_gen and pop_fit subscripts.
| A disease progression model, based on the concentration of drug in the central compartment.
| The amount in the central compartment is determined by CL/V, which has been previously estimated for each individual.
| The disease compartment increases linearly over time, but is initially reduced by the drug concentration.

:Keywords: PD; Pharmacodynamics; one compartment model; linear disease progression

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

:Diagram: 


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


Comparison
**********



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


.. code-block:: pyml

    86.0288



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


.. code-block:: pyml

    83.4072



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]
=====================


No Variance f[X] values to compare.

Outputs
*******



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


.. code-block:: pyml

    f[DIS_BASE] = 200.7692
    f[ALPHA] = 2.5184
    f[BETA] = 0.3547
    f[ANOISE] = 2.0000



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


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


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


* Gen: :ref:`dp_compartmental_gen` (gen)
* Fit: :ref:`dp_compartmental_fit` (fit)

Inputs
******



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

.. code-block:: pyml

    f[DIS_BASE] = 200.0000
    f[ALPHA] = 2.5000
    f[BETA] = 0.3000
    f[ANOISE] = 1.0000



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

.. code-block:: pyml

    f[DIS_BASE] = 250.0000
    f[ALPHA] = 2.0000
    f[BETA] = 0.2000
    f[ANOISE] = 2.0000

