:orphan: 





.. _indirect_pd_fit:



Indirect PKPD Model
###################

[Generated automatically as a Fitting summary]

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


:Name: indirect_pd

:Title: Indirect PKPD Model

:Author: PoPy for PK/PD

:Abstract: 

| A indirect PD Model, based on the amount of drug in the body, delayed by two lag compartments
| The amount in the central compartment is determined by K, which has been estimated for each individual.
| The amount in the central compartment influences the rate of removal of a biomarker (KOUT).

:Keywords: pd; one compartment model; indirect; delay compartment

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

:Diagram: 


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


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[BASE]                  500.0000        799.9957      299.9957         0.6000
f[KOUT]                    0.1000          0.0300        0.0700         0.7001
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[ANOISE]                  5.0000          0.4633        4.5367         0.9073
===============  ================  ==============  ============  =============

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

    -53.8626


which required 1.27 iterations and took 11.24 seconds

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


.. code-block:: pyml

    f[BASE] = 799.9957
    f[KOUT] = 0.0300
    f[ANOISE] = 0.4633



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <indirect_pd_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[BASE] = 500.0000
    f[KOUT] = 0.1000
    f[ANOISE] = 5.0000

