emax_disease_progression_compartmental_model

[Generated automatically as a Fitting summary]

Model Description

Name:: dp_emax
Title:: emax_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.
The effect of the drug concentration is limited by an emax model

Keywords:: PD; Pharmacodynamics; one compartment model; linear disease progression; emax; E50
Input Script:: dp_emax_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[EMAX]	20.0000	81.5395	61.5395	3.0770
f[E50]	15.0000	953.4332	938.4332	62.5622
f[ALPHA]	2.0000	2.5036	0.5036	0.2518
f[BETA]	0.1000	9.5370	9.4370	94.3700

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[S0]	250.0000	201.7859	48.2141	0.1929
f[ANOISE]	2.0000	0.9323	1.0677	0.5338

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

85.9739

which required 1.30 iterations and took 14.72 seconds

Fitted f[X] values (after fitting)

f[S0] = 201.7859
f[EMAX] = 81.5395
f[E50] = 953.4332
f[ALPHA] = 2.5036
f[BETA] = 9.5370
f[ANOISE] = 0.9323

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[S0] = 250.0000
f[EMAX] = 20.0000
f[E50] = 15.0000
f[ALPHA] = 2.0000
f[BETA] = 0.1000
f[ANOISE] = 2.0000