:orphan: 





.. _dp_placebo_gen:



placebo_disease_progression_model
#################################

[Generated automatically as a Generation summary]

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


:Name: dp_placebo

:Title: placebo_disease_progression_model

:Author: Andrew Cristinacce @ PoPy for PK/PD

:Abstract: 

| Specifies both the pop_gen and pop_fit subscripts.
| A exponential 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 compartment increases exponentially over time, but is initially reduced by the drug concentration.
| A placebo effect is also included. This decreases over time and the individual placebo effects can shift in both directions.

:Keywords: PD; Pharmacodynamics; one compartment model; exponential disease progression; placebo

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

:Diagram: 


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


Outputs
*******



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


.. list-table:: 
    :width: 90%

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

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


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

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

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

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

:Predictions: :download:`px_params.csv (gen) <dp_placebo_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[PLAC] = -20.0000
    f[ALPHA] = 0.0500
    f[BETA] = 0.1000
    f[ANOISE] = 2.0000

