exponential_disease_progression_model

[Generated automatically as a Fitting summary]

Model Description

Name:

dp_exponential

Title:

exponential_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.

Keywords:

PD; Pharmacodynamics; one compartment model; exponential disease progression

Input Script:

dp_exponential_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ALPHA]	0.1000	0.0671	0.0329	0.3285
f[BETA]	0.2000	0.3543	0.1543	0.7714
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	200.7663	49.2337	0.1969

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

83.4329

which required 1.22 iterations and took 20.18 seconds

Fitted f[X] values (after fitting)

f[DIS_BASE] = 200.7663
f[ALPHA] = 0.0671
f[BETA] = 0.3543
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] = 0.1000
f[BETA] = 0.2000
f[ANOISE] = 2.0000