:orphan: 





.. _dp_iov_fit:



Disease progression with inter-occasional variance
##################################################

[Generated automatically as a Fitting summary]

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


:Name: dp_iov

:Title: Disease progression with inter-occasional variance

:Author: Andrew Cristinacce @ PoPy for PK/PD

:Abstract: 

| Specifies both the pop_gen and pop_fit subscripts.
| Drug concentration defined by one compartment model with first order absorption, using pre-defined PK parameters.
| Individuals are split into four dose groups.
| The concentration in the CENTRAL compartment affects the drug effect, which is an emax model.
| The disease progression model is affected by the concentration. Higher concentrations reduce the disease status
| Inter-occasional variance is included in the LEVEL_PARAMS section and includes 3 occasions.

:Keywords: one compartment model; iov; inter occasional variance; emax; absorption

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

:Diagram: 


.. thumbnail:: dp_iov_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]                  450.0000        386.5315       63.4685         0.1410
f[ALPHA]                   0.0200          0.0267        0.0067         0.3347
f[EC50]                    1.0000          2.0606        1.0606         1.0606
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[EMAX]                    0.1000          0.4164        0.3164         3.1637
f[ANOISE]                  0.0500          0.1021        0.0521         1.0429
===============  ================  ==============  ============  =============

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


=====================  ================  ==============  ============  =============
Variable Name            Starting Value    Fitted Value    Abs Change    Prop Change
=====================  ================  ==============  ============  =============
f[BASE_isv]                      0.2000          0.0768        0.1232         0.6162
f[BASE_isv;ALPHA_isv]            0.0000          0.0512        0.0512       INF
f[BASE_isv;EC50_isv]             0.0000         -0.0094        0.0094       INF
f[ALPHA_isv;BASE_isv]            0.0000          0.0512        0.0512       INF
f[ALPHA_isv]                     0.1000          0.0647        0.0353         0.3530
f[ALPHA_isv;EC50_isv]            0.0000          0.1058        0.1058       INF
f[EC50_isv;BASE_isv]             0.0000         -0.0094        0.0094       INF
f[EC50_isv;ALPHA_isv]            0.0000          0.1058        0.1058       INF
f[EC50_isv]                      0.5000          0.6191        0.1191         0.2382
f[BASE_iov]                      0.0200          0.0416        0.0216         1.0795
f[BASE_iov;ALPHA_iov]            0.0000         -0.0718        0.0718       INF
f[ALPHA_iov;BASE_iov]            0.0000         -0.0718        0.0718       INF
f[ALPHA_iov]                     0.0100          0.1241        0.1141        11.4089
=====================  ================  ==============  ============  =============

Individual simulated (sim) plots
================================



.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000002.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000003.svg
    :width: 200px


Alternatively see :ref:`dp_iov_simulated_sim_plots`

Population simulated (sim) plots
================================


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    -2623.2794


which required 1.30 iterations and took 593.20 seconds

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


.. code-block:: pyml

    f[BASE] = 386.5315
    f[ALPHA] = 0.0267
    f[EMAX] = 0.4164
    f[EC50] = 2.0606
    f[ANOISE] = 0.1021
    f[BASE_isv,ALPHA_isv,EC50_isv] = [
        [ 0.0768, 0.0512, -0.0094 ],
        [ 0.0512, 0.0647, 0.1058 ],
        [ -0.0094, 0.1058, 0.6191 ],
    ]
    f[BASE_iov,ALPHA_iov] = [
        [ 0.0416, -0.0718 ],
        [ -0.0718, 0.1241 ],
    ]



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <dp_iov_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] = 450.0000
    f[ALPHA] = 0.0200
    f[EMAX] = 0.1000
    f[EC50] = 1.0000
    f[ANOISE] = 0.0500
    f[BASE_isv,ALPHA_isv,EC50_isv] = [
        [ 0.2000, 0.0000, 0.0000 ],
        [ 0.0000, 0.1000, 0.0000 ],
        [ 0.0000, 0.0000, 0.5000 ],
    ]
    f[BASE_iov,ALPHA_iov] = [
        [ 0.0200, 0.0000 ],
        [ 0.0000, 0.0100 ],
    ]

