tumour_growth_model

[Generated automatically as a Fitting summary]

Model Description

Name:

dp_tumour

Title:

tumour_growth_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; absorption; tumour growth

Input Script:

dp_tumour_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KIN]	0.0500	0.0246	0.0254	0.5088
f[KOUT]	0.1000	0.0529	0.0471	0.4712

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ANOISE]	0.2000	0.4604	0.2604	1.3021

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

-55.1167

which required 1.17 iterations and took 13.22 seconds

Fitted f[X] values (after fitting)

f[KIN] = 0.0246
f[KOUT] = 0.0529
f[ANOISE] = 0.4604

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[KIN] = 0.0500
f[KOUT] = 0.1000
f[ANOISE] = 0.2000