binary_emax_PD_model

[Generated automatically as a Fitting summary]

Model Description

Name:: emax_binary
Title:: binary_emax_PD_model
Author:: PoPy for PK/PD
Abstract:

An emax PK/PD Model, based on the amount of drug in the body.
The two compartment PK model with previously estimated values of K, K21 and K12 for each individual.
The amount in the central compartment influences the effect compartment along with EMAX (the maximum effect) and EC50 (the amount at which the effect is half the maximum)

Keywords:: PD; Pharmacodynamics; two compartment model; emax; EC50; binary response
Input Script:: emax_binary_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[EMAX]	100.0000	80.1058	19.8942	0.1989
f[E50]	10.0000	20.0475	10.0475	1.0048

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ANOISE]	0.5000	0.9466	0.4466	0.8931

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

88.9284

which required 1.17 iterations and took 13.06 seconds

Fitted f[X] values (after fitting)

f[EMAX] = 80.1058
f[E50] = 20.0475
f[ANOISE] = 0.9466

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[EMAX] = 100.0000
f[E50] = 10.0000
f[ANOISE] = 0.5000