:orphan: 





.. _dp_emax_fit:



emax_disease_progression_compartmental_model
############################################

[Generated automatically as a Fitting summary]

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


:Name: dp_emax

:Title: emax_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.
| The effect of the drug concentration is limited by an emax model

:Keywords: PD; Pharmacodynamics; one compartment model; linear disease progression; emax; E50

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

:Diagram: 


.. thumbnail:: dp_emax_fit.pyml_output/compartment_diagram.svg
    :width: 200px


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[EMAX]                   20.0000         81.5395       61.5395         3.0770
f[E50]                    15.0000        953.4332      938.4332        62.5622
f[ALPHA]                   2.0000          2.5036        0.5036         0.2518
f[BETA]                    0.1000          9.5370        9.4370        94.3700
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[S0]                    250.0000        201.7859       48.2141         0.1929
f[ANOISE]                  2.0000          0.9323        1.0677         0.5338
===============  ================  ==============  ============  =============

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




Population simulated (sim) plots
================================


.. list-table:: 
    :width: 90%

    * - .. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
            :width: 200px
      - indOBS_vs_TIME

Outputs
*******



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

.. code-block:: pyml

    85.9739


which required 1.30 iterations and took 14.72 seconds

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


.. code-block:: pyml

    f[S0] = 201.7859
    f[EMAX] = 81.5395
    f[E50] = 953.4332
    f[ALPHA] = 2.5036
    f[BETA] = 9.5370
    f[ANOISE] = 0.9323



Fitted parameter .csv files
===========================


:Fixed Effects: :download:`fx_params.csv (fit) <dp_emax_fit.pyml_output/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <dp_emax_fit.pyml_output/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <dp_emax_fit.pyml_output/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <dp_emax_fit.pyml_output/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <dp_emax_fit.pyml_output/solN/px_params.csv>`

:Likelihoods: :download:`lx_params.csv (fit) <dp_emax_fit.pyml_output/solN/lx_params.csv>`



Inputs
******


:Input Data: :download:`cx_obs_params.csv <cx_obs_params.csv>`


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


.. code-block:: pyml

    f[S0] = 250.0000
    f[EMAX] = 20.0000
    f[E50] = 15.0000
    f[ALPHA] = 2.0000
    f[BETA] = 0.1000
    f[ANOISE] = 2.0000

