:orphan: 





.. _emax_nondiff_fit:



Direct Effect Emax PD Model
###########################

[Generated automatically as a Fitting summary]

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


:Name: emax_nondiff

:Title: Direct Effect Emax PD Model

:Author: PoPy for PK/PD

:Abstract: 

| A direct effect emax PK/PD Model, based on the concentration of drug in the body.
| The one compartment PK model uses previously estimated values of CL and V for each individual
| The concentration in the central compartment influences the effect.
| The effect is not a compartment and so does not increase over time. It is dependent on the baseline effect, the concentration in the central compartment, the maximum effect (emax) and concentration at which the effect is half the maximum (EC50).

:Keywords: PD; Pharmacodynamics; one compartment model; emax; EC50; baseline effect

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

:Diagram: 


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


Comparison
**********



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




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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[EMAX]                   80.0000        522.6699      442.6699         5.5334
f[E50]                    50.0000        322.8391      272.8391         5.4568
f[EBASE]                   5.0000         10.0149        5.0149         1.0030
f[ANOISE]                  2.0000          0.4602        1.5398         0.7699
===============  ================  ==============  ============  =============

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

    -54.8713


which required 1.30 iterations and took 11.02 seconds

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


.. code-block:: pyml

    f[EMAX] = 522.6699
    f[E50] = 322.8391
    f[EBASE] = 10.0149
    f[ANOISE] = 0.4602



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <emax_nondiff_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[EMAX] = 80.0000
    f[E50] = 50.0000
    f[EBASE] = 5.0000
    f[ANOISE] = 2.0000

