Disease progression with inter-occasional variance

[Generated automatically as a Fitting 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_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[BASE]	450.0000	386.5315	63.4685	0.1410
f[ALPHA]	0.0200	0.0267	0.0067	0.3347
f[EC50]	1.0000	2.0606	1.0606	1.0606

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[EMAX]	0.1000	0.4164	0.3164	3.1637
f[ANOISE]	0.0500	0.1021	0.0521	1.0429

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[BASE_isv]	0.2000	0.0768	0.1232	0.6162
f[BASE_isv;ALPHA_isv]	0.0000	0.0512	0.0512	INF
f[BASE_isv;EC50_isv]	0.0000	-0.0094	0.0094	INF
f[ALPHA_isv;BASE_isv]	0.0000	0.0512	0.0512	INF
f[ALPHA_isv]	0.1000	0.0647	0.0353	0.3530
f[ALPHA_isv;EC50_isv]	0.0000	0.1058	0.1058	INF
f[EC50_isv;BASE_isv]	0.0000	-0.0094	0.0094	INF
f[EC50_isv;ALPHA_isv]	0.0000	0.1058	0.1058	INF
f[EC50_isv]	0.5000	0.6191	0.1191	0.2382
f[BASE_iov]	0.0200	0.0416	0.0216	1.0795
f[BASE_iov;ALPHA_iov]	0.0000	-0.0718	0.0718	INF
f[ALPHA_iov;BASE_iov]	0.0000	-0.0718	0.0718	INF
f[ALPHA_iov]	0.0100	0.1241	0.1141	11.4089

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-2623.2794

which required 1.30 iterations and took 593.20 seconds

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 ],
]

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[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 ],
]