:orphan: 





.. _dp_linear_fit:



linear_disease_progression_model
################################

[Generated automatically as a Fitting summary]

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


:Name: dp_linear

:Title: linear_disease_progression_model

:Author: Andrew Cristinacce @ PoPy for PK/PD

:Abstract: 

| Specifies both the pop_gen and pop_fit subscripts.
| A disease progression model, based on the concentration of drug in the central compartment.
| The amount in the central compartment is determined by CL/V, which has been previously estimated for each individual.
| The disease increases linearly over time, but is initially reduced by the drug concentration.

:Keywords: PD; Pharmacodynamics; one compartment model; linear disease progression

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

:Diagram: 


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


Comparison
**********



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




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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[ALPHA]                   0.2000          0.3014        0.1014         0.5070
f[BETA]                    0.5000          0.7293        0.2293         0.4587
===============  ================  ==============  ============  =============

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

    85.3044


which required 1.6 iterations and took 10.54 seconds

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


.. code-block:: pyml

    f[ALPHA] = 0.3014
    f[BETA] = 0.7293



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <dp_linear_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[ALPHA] = 0.2000
    f[BETA] = 0.5000

