linear_disease_progression_compartmental_model

[Generated automatically as a Fitting 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_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ALPHA]	2.0000	2.5184	0.5184	0.2592
f[BETA]	0.2000	0.3547	0.1547	0.7735
f[ANOISE]	2.0000	2.0000	0.0000	0.0000

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[DIS_BASE]	250.0000	200.7692	49.2308	0.1969

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

83.4072

which required 1.14 iterations and took 10.82 seconds

Fitted f[X] values (after fitting)

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

Fitted parameter .csv files

Fixed Effects:

fx_params.csv (fit)

Random Effects:

rx_params.csv (fit)

Model params:

mx_params.csv (fit)

State values:

sx_params.csv (fit)

Predictions:

px_params.csv (fit)

Likelihoods:

lx_params.csv (fit)

Inputs

Input Data:

cx_obs_params.csv

Starting f[X] values (before fitting)

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