linear_disease_progression_model

[Generated automatically as a Fitting summary]

Model Description

Name:

dp_linear

Title:

linear_disease_progression_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 increases linearly over time, but is initially reduced by the drug concentration.

Keywords:

PD; Pharmacodynamics; one compartment model; linear disease progression

Input Script:

dp_linear_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ALPHA]	0.2000	0.3014	0.1014	0.5070
f[BETA]	0.5000	0.7293	0.2293	0.4587

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

85.3044

which required 1.6 iterations and took 10.54 seconds

Fitted f[X] values (after fitting)

f[ALPHA] = 0.3014
f[BETA] = 0.7293

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[ALPHA] = 0.2000
f[BETA] = 0.5000