:orphan: 





.. _direct_pd_simul_fit:



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

[Generated automatically as a Fitting summary]

Inputs
******



Description
===========

:Name: direct_pd_simul

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

:Author: Wright Dose Ltd

: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>`

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

:Diagram: 


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


Initial fixed effect estimates
==============================

.. 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



Outputs
*******



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

.. code-block:: pyml

    386.5948


which required 1.30 iterations and took 5.39 seconds

Final fitted fixed effects
==========================

.. code-block:: pyml

    f[CL] = 2.0029
    f[V] = 48.1367
    f[BASE] = 799.2158
    f[KOUT] = 0.0288
    f[PK_ANOISE] = 0.5095
    f[PD_ANOISE] = 8.2249



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


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

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

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

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

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



Plots
*****



Dense sim plots
===============



.. thumbnail:: images/fit_dense/000001.svg
    :width: 200px


Alternatively see :ref:`direct_pd_simul_dense_sim_plots`

Comparison
**********



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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[CL]                      5.0000          2.0029         0.5994        2.9971
f[V]                      15.0000         48.1367         2.2091       33.1367
f[BASE]                  500.0000        799.2158         0.5984      299.2158
f[KOUT]                    0.1000          0.0288         0.7119        0.0712
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PK_ANOISE]               5.0000          0.5095         0.8981        4.4905
f[PD_ANOISE]               5.0000          8.2249         0.6450        3.2249
===============  ================  ==============  =============  ============

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


