:orphan: 





.. _indirect_pd_pop_gen:



Indirect_PKPD_model
###################

[Generated automatically as a Generation summary]

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


:Name: indirect_pd_pop

:Title: Indirect_PKPD_model

:Author: PoPy for PK/PD

:Abstract: 

| A indirect (i.e. uses delay compartments) PD Model, based on the amount of drug in the body, delayed by two lag compartments
| The amount in the central compartment is determined by K, which has been estimated for each individual.
| The amount in the central compartment influences the rate of removal of a biomarker (KOUT).

:Keywords: pd; one compartment model; indirect; delay compartment

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

:Diagram: 


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


Outputs
*******



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



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


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


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


Alternatively see :ref:`indirect_pd_pop_simulated_sim_plots`

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


(No population graphs were requested.)

Generated parameter .csv files
==============================


:Fixed Effects: :download:`fx_params.csv (gen) <indirect_pd_pop_gen.pyml_output/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (gen) <indirect_pd_pop_gen.pyml_output/rx_params.csv>`

:Model params: :download:`mx_params.csv (gen) <indirect_pd_pop_gen.pyml_output/mx_params.csv>`

:State values: :download:`sx_params.csv (gen) <indirect_pd_pop_gen.pyml_output/sx_params.csv>`

:Predictions: :download:`px_params.csv (gen) <indirect_pd_pop_gen.pyml_output/px_params.csv>`


:Observations: :download:`synthetic_data.csv (gen) <synthetic_data.csv>`


Inputs
******



True f[X] values (for simulation)
=================================


.. code-block:: pyml

    f[BASE] = 800.0000
    f[KOUT] = 0.0300
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.5000
    f[BASE_isv,KOUT_isv] = [
        [ 0.0500, 0.0000 ],
        [ 0.0000, 0.0100 ],
    ]

