: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
    f[V] = 15
    f[BASE] = 500
    f[KOUT] = 0.1
    f[PK_ANOISE] = 5
    f[PD_ANOISE] = 5



Outputs
*******



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

.. code-block:: pyml

    -208.115820594


which required 5 iterations and took 0.81 seconds

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

.. code-block:: pyml

    f[CL] = 1.9992
    f[V] = 51.005
    f[BASE] = 799.98
    f[KOUT] = 0.030588
    f[PK_ANOISE] = 0.49676
    f[PD_ANOISE] = 0.25265



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/000000.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]                 1.99915                 5         0.600169     3.00085
f[V]                 51.0055                 15         2.40037     36.0055
f[BASE]             799.977                 500         0.599953   299.977
f[KOUT]               0.0305875               0.1       0.694125     0.0694125
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PK_ANOISE]           0.496756                 5       0.900649       4.50324
f[PD_ANOISE]           0.252646                 5       0.949471       4.74735
===============  ==============  ================  =============  ============

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


