: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

    424.9294


which required 8 iterations and took 1.11 seconds

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

.. code-block:: pyml

    f[CL] = 1.9902
    f[V] = 50.9567
    f[BASE] = 800.8924
    f[KOUT] = 0.0306
    f[PK_ANOISE] = 0.5071
    f[PD_ANOISE] = 10.0000



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      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[CL]                    1.9902            5.0000         0.6020        3.0098
f[V]                    50.9567           15.0000         2.3971       35.9567
f[BASE]                800.8924          500.0000         0.6018      300.8924
f[KOUT]                  0.0306            0.1000         0.6938        0.0694
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PK_ANOISE]             0.5071            5.0000         0.8986        4.4929
f[PD_ANOISE]            10.0000            5.0000         1.0000        5.0000
===============  ==============  ================  =============  ============

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


