Disease progression with inter-occasional variance

[Generated automatically as a Tutorial summary]

Model Description

Name:

dp_iov

Title:

Disease progression with inter-occasional variance

Author:

Andrew Cristinacce @ PoPy for PK/PD

Abstract:

Specifies both the pop_gen and pop_fit subscripts.

Drug concentration defined by one compartment model with first order absorption, using pre-defined PK parameters.

Individuals are split into four dose groups.

The concentration in the CENTRAL compartment affects the drug effect, which is an emax model.

The disease progression model is affected by the concentration. Higher concentrations reduce the disease status

Inter-occasional variance is included in the LEVEL_PARAMS section and includes 3 occasions.

Keywords:

one compartment model; iov; inter occasional variance; emax; absorption

Input Script:

dp_iov.pyml

Diagram:

Comparison

True objective value

-2619.7497

Final fitted objective value

-2623.2794

Compare Main f[X]

Name	Initial	Fitted	True	Abs. Error	Prop. Error
f[BASE]	450	387	400	1.35e+01	3.37%
f[ALPHA]	0.02	0.0267	0.025	1.69e-03	6.78%
f[EC50]	1	2.06	1.8	2.61e-01	14.48%

Compare Noise f[X]

Name	Initial	Fitted	True	Abs. Error	Prop. Error
f[EMAX]	0.1	0.416	0.4	1.64e-02	4.09%
f[ANOISE]	0.05	0.102	0.1	2.15e-03	2.15%

Compare Variance f[X]

Name	Initial	Fitted	True	Abs. Error	Prop. Error
f[BASE_isv]	0.2	0.0768	0.1	2.32e-02	23.23%
f[BASE_isv;ALPHA_isv]	0	0.0512	0	5.12e-02	inf
f[BASE_isv;EC50_isv]	0	-0.0094	0	9.40e-03	inf
f[ALPHA_isv;BASE_isv]	0	0.0512	0	5.12e-02	inf
f[ALPHA_isv]	0.1	0.0647	0.05	1.47e-02	29.40%
f[ALPHA_isv;EC50_isv]	0	0.106	0	1.06e-01	inf
f[EC50_isv;BASE_isv]	0	-0.0094	0	9.40e-03	inf
f[EC50_isv;ALPHA_isv]	0	0.106	0	1.06e-01	inf
f[EC50_isv]	0.5	0.619	0.9	2.81e-01	31.21%
f[BASE_iov]	0.02	0.0416	0.03	1.16e-02	38.63%
f[BASE_iov;ALPHA_iov]	0	-0.0718	0	7.18e-02	inf
f[ALPHA_iov;BASE_iov]	0	-0.0718	0	7.18e-02	inf
f[ALPHA_iov]	0.01	0.124	0.02	1.04e-01	520.44%

Outputs

Fitted f[X] values (after fitting)

f[BASE] = 386.5315
f[ALPHA] = 0.0267
f[EMAX] = 0.4164
f[EC50] = 2.0606
f[ANOISE] = 0.1021
f[BASE_isv,ALPHA_isv,EC50_isv] = [
    [ 0.0768, 0.0512, -0.0094 ],
    [ 0.0512, 0.0647, 0.1058 ],
    [ -0.0094, 0.1058, 0.6191 ],
]
f[BASE_iov,ALPHA_iov] = [
    [ 0.0416, -0.0718 ],
    [ -0.0718, 0.1241 ],
]

Generated data .csv file

Synthetic Data:

synthetic_data.csv

Gen and Fit Summaries

• Gen: Disease progression with inter-occasional variance (gen)

• Fit: Disease progression with inter-occasional variance (fit)

Inputs

True f[X] values (for simulation)

f[BASE] = 400.0000
f[ALPHA] = 0.0250
f[EMAX] = 0.4000
f[EC50] = 1.8000
f[ANOISE] = 0.1000
f[BASE_isv,ALPHA_isv,EC50_isv] = [
    [ 0.1000, 0.0000, 0.0000 ],
    [ 0.0000, 0.0500, 0.0000 ],
    [ 0.0000, 0.0000, 0.9000 ],
]
f[BASE_iov,ALPHA_iov] = [
    [ 0.0300, 0.0000 ],
    [ 0.0000, 0.0200 ],
]

Starting f[X] values (before fitting)

f[BASE] = 450.0000
f[ALPHA] = 0.0200
f[EMAX] = 0.1000
f[EC50] = 1.0000
f[ANOISE] = 0.0500
f[BASE_isv,ALPHA_isv,EC50_isv] = [
    [ 0.2000, 0.0000, 0.0000 ],
    [ 0.0000, 0.1000, 0.0000 ],
    [ 0.0000, 0.0000, 0.5000 ],
]
f[BASE_iov,ALPHA_iov] = [
    [ 0.0200, 0.0000 ],
    [ 0.0000, 0.0100 ],
]