placebo_disease_progression_model

[Generated automatically as a Tutorial 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:

dp_placebo.pyml

Diagram:

Comparison

True objective value

228.4846

Final fitted objective value

299.1120

Compare Main f[X]

Name

Initial

Fitted

True

Abs. Error

Prop. Error

f[PLAC]

-1

-20.2

-20

1.75e-01

0.87%

f[ALPHA]

0.1

0.0499

0.05

6.71e-05

0.13%

f[BETA]

0.2

0.00543

0.1

9.46e-02

94.57%

Compare Noise f[X]

Name

Initial

Fitted

True

Abs. Error

Prop. Error

f[ANOISE]

1

1.18

2

8.25e-01

41.25%

Compare Variance f[X]

No Variance f[X] values to compare.

Outputs

Fitted f[X] values (after fitting)

f[PLAC] = -20.1747
f[ALPHA] = 0.0499
f[BETA] = 0.0054
f[ANOISE] = 1.1751

Generated data .csv file

Synthetic Data:

synthetic_data.csv

Gen and Fit Summaries

Inputs

True f[X] values (for simulation)

f[PLAC] = -20.0000
f[ALPHA] = 0.0500
f[BETA] = 0.1000
f[ANOISE] = 2.0000

Starting f[X] values (before fitting)

f[PLAC] = -1.0000
f[ALPHA] = 0.1000
f[BETA] = 0.2000
f[ANOISE] = 1.0000