linear_disease_progression_compartmental_model

[Generated automatically as a Tutorial summary]

Model Description

Name:: dp_compartmental
Title:: linear_disease_progression_compartmental_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 compartment increases linearly over time, but is initially reduced by the drug concentration.

Keywords:: PD; Pharmacodynamics; one compartment model; linear disease progression
Input Script:: dp_compartmental.pyml
Diagram:

Comparison

True objective value

86.0288

Final fitted objective value

83.4072

Compare Main f[X]

Name	Initial	Fitted	True	Abs. Error	Prop. Error
f[ALPHA]	2	2.52	2.5	1.84e-02	0.74%
f[BETA]	0.2	0.355	0.3	5.47e-02	18.23%
f[ANOISE]	2	2	1	1.00e+00	100.00%

Compare Noise f[X]

Name	Initial	Fitted	True	Abs. Error	Prop. Error
f[DIS_BASE]	250	201	200	7.69e-01	0.38%

Compare Variance f[X]

No Variance f[X] values to compare.

Outputs

Fitted f[X] values (after fitting)

f[DIS_BASE] = 200.7692
f[ALPHA] = 2.5184
f[BETA] = 0.3547
f[ANOISE] = 2.0000

Generated data .csv file

Synthetic Data:: synthetic_data.csv

Gen and Fit Summaries

Gen: linear_disease_progression_compartmental_model (gen)
Fit: linear_disease_progression_compartmental_model (fit)

Inputs

True f[X] values (for simulation)

f[DIS_BASE] = 200.0000
f[ALPHA] = 2.5000
f[BETA] = 0.3000
f[ANOISE] = 1.0000

Starting f[X] values (before fitting)

f[DIS_BASE] = 250.0000
f[ALPHA] = 2.0000
f[BETA] = 0.2000
f[ANOISE] = 2.0000