:orphan: 





.. _direct_pd_simul_fit:



Direct PD Model Simultaneous PK/PD Parameter fit
################################################

[Generated automatically as a Fitting summary]

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


:Name: direct_pd_simul

:Title: Direct PD Model Simultaneous PK/PD Parameter fit

:Author: PoPy for PK/PD

:Abstract: 

| A simple direct PD Model, based on the amount of drug in the body. That simultaneously fits PK and PD parameters.
| The amount in the central compartment is determined by K, which has been previously estimated for each individual.
| The amount in the central compartment influences the rate of removal of a biomarker (KOUT).

:Keywords: pd; one compartment model; direct

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

:Diagram: 


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


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[CL]                      5.0000          2.0038        2.9962         0.5992
f[V]                      15.0000         48.1868       33.1868         2.2125
f[BASE]                  500.0000        799.1967      299.1967         0.5984
f[KOUT]                    0.1000          0.0288        0.0712         0.7116
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PK_ANOISE]               5.0000          0.5088        4.4912         0.8982
f[PD_ANOISE]               5.0000          8.2246        3.2246         0.6449
===============  ================  ==============  ============  =============

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

    386.5798


which required 1.30 iterations and took 11.40 seconds

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


.. code-block:: pyml

    f[CL] = 2.0038
    f[V] = 48.1868
    f[BASE] = 799.1967
    f[KOUT] = 0.0288
    f[PK_ANOISE] = 0.5088
    f[PD_ANOISE] = 8.2246



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <direct_pd_simul_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[CL] = 5.0000
    f[V] = 15.0000
    f[BASE] = 500.0000
    f[KOUT] = 0.1000
    f[PK_ANOISE] = 5.0000
    f[PD_ANOISE] = 5.0000

