:orphan: 





.. _emax_biomarker_fit:



emax_marker_model
#################

[Generated automatically as a Fitting summary]

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


:Name: emax_biomarker

:Title: emax_marker_model

:Author: PoPy for PK/PD

:Abstract: 

| An emax 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 estimated for each individual.
| The concentration in the central compartment influences the rate of removal of a biomarker (KOUT).

:Keywords: PD; Pharmacodynamics; one compartment model; KIN; KOUT; emax; biomarker

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

:Diagram: 


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


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[KIN]                    15.0000         87.8471       72.8471         4.8565
f[KOUT]                    0.1000          0.0490        0.0510         0.5099
f[EMAX]                   90.0000         27.3312       62.6688         0.6963
f[E50]                    25.0000         95.9468       70.9468         2.8379
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[ANOISE]                  5.0000          1.6065        3.3935         0.6787
===============  ================  ==============  ============  =============

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

    193.0522


which required 1.30 iterations and took 12.93 seconds

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


.. code-block:: pyml

    f[KIN] = 87.8471
    f[KOUT] = 0.0490
    f[EMAX] = 27.3312
    f[E50] = 95.9468
    f[ANOISE] = 1.6065



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <emax_biomarker_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[KIN] = 15.0000
    f[KOUT] = 0.1000
    f[EMAX] = 90.0000
    f[E50] = 25.0000
    f[ANOISE] = 5.0000

