• Language: en

Body Weight Covariate

[Generated automatically as a Fitting summary]

Model Description

Name:weight_covariate
Title:Body Weight Covariate
Author:PoPy for PK/PD
Abstract:
One compartment model with absorption compartment and CL/V parametrisation.
There are no random effects here. Each individual just has a different weight.
The weight is a covariate for the m[CL] clearance parameter for each individual.
Only the f[WT_EFFECT] and f[V] fixed effect parameters are estimated, other f[X] are fixed.
Keywords:one compartment model; dep_one_cmp_cl; weight; covariate effect
Input Script:weight_covariate_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name Starting Value Fitted Value Abs Change Prop Change
f[V] 15.0000 20.2610 5.2610 0.3507
f[WT_EFFECT] 1.0000 0.6657 0.3343 0.3343

Compare Noise f[X]

Compare Variance f[X]

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-486.0798

which required 1.6 iterations and took 4.29 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.2610
f[PNOISE] = 0.1000
f[ANOISE] = 0.0500
f[WT_EFFECT] = 0.6657

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[KA] = 0.3000
f[CL] = 3.0000
f[V] = 15.0000
f[PNOISE] = 0.1000
f[ANOISE] = 0.0500
f[WT_EFFECT] = 1.0000
Back to Top