Nonmem ADVAN3 TRANS6 example

[Generated automatically as a Fitting summary]

Model Description

Name:: advan3trans6_pop
Title:: Nonmem ADVAN3 TRANS6 example
Author:: PoPy for PK/PD
Abstract:

ADVAN 3 TRANS6 problem. Central compartment with a peripheral compartment.
trans 6 parameters:-
* K21 is the rate from the peripheral to the central compartment
* ALPHA
* BETA
map to advan3 parameters:-
* K21 = K21
* K12 = ALPHA+BETA-K21-K
* KE = APLHA*BETA/K21
ALPHA<K21<BETA. This must be true for model to work.

Keywords:: advan3; trans6; two compartment model
Input Script:: advan3trans6_pop_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ALPHA]	0.1000	0.1970	0.0970	0.9705
f[DIFFALPHAK21]	0.2000	0.2996	0.0996	0.4978
f[DIFFK21BETA]	0.2000	0.3022	0.1022	0.5108

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE]	0.0500	0.0099	0.0401	0.8015
f[ANOISE]	0.0500	0.0096	0.0404	0.8077

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ALPHA_isv]	0.0400	0.0968	0.0568	1.4192
f[ALPHA_isv;DIFFALPHAK21_isv]	0.0000	-0.0011	0.0011	INF
f[ALPHA_isv;DIFFK21BETA_isv]	0.0000	-0.0008	0.0008	INF
f[DIFFALPHAK21_isv;ALPHA_isv]	0.0000	-0.0011	0.0011	INF
f[DIFFALPHAK21_isv]	0.0800	0.0525	0.0275	0.3436
f[DIFFALPHAK21_isv;DIFFK21BETA_isv]	0.0000	0.0028	0.0028	INF
f[DIFFK21BETA_isv;ALPHA_isv]	0.0000	-0.0008	0.0008	INF
f[DIFFK21BETA_isv;DIFFALPHAK21_isv]	0.0000	0.0028	0.0028	INF
f[DIFFK21BETA_isv]	0.0700	0.0124	0.0576	0.8232

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-3364.1589

which required 1.27 iterations and took 159.10 seconds

Fitted f[X] values (after fitting)

f[ALPHA] = 0.1970
f[DIFFALPHAK21] = 0.2996
f[DIFFK21BETA] = 0.3022
f[PNOISE] = 0.0099
f[ANOISE] = 0.0096
f[ALPHA_isv,DIFFALPHAK21_isv,DIFFK21BETA_isv] = [
    [ 0.0968, -0.0011, -0.0008 ],
    [ -0.0011, 0.0525, 0.0028 ],
    [ -0.0008, 0.0028, 0.0124 ],
]

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[ALPHA] = 0.1000
f[DIFFALPHAK21] = 0.2000
f[DIFFK21BETA] = 0.2000
f[PNOISE] = 0.0500
f[ANOISE] = 0.0500
f[ALPHA_isv,DIFFALPHAK21_isv,DIFFK21BETA_isv] = [
    [ 0.0400, 0.0000, 0.0000 ],
    [ 0.0000, 0.0800, 0.0000 ],
    [ 0.0000, 0.0000, 0.0700 ],
]