placebo_disease_progression_model

[Generated automatically as a Fitting 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_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PLAC]	-1.0000	-20.1747	19.1747	19.1747
f[ALPHA]	0.1000	0.0499	0.0501	0.5007
f[BETA]	0.2000	0.0054	0.1946	0.9728

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ANOISE]	1.0000	1.1751	0.1751	0.1751

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

299.1120

which required 1.30 iterations and took 16.09 seconds

Fitted f[X] values (after fitting)

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

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