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Depot + One compartment PK with BLQ

[Generated automatically as a Fitting summary]

Model Description

Name:

blq_pk

Title:

Depot + One compartment PK with BLQ

Author:

PoPy for PK/PD

Abstract:

Depot One Comp PK model, with BLQ (below level of quantification) observations.
Keywords:

tutorial; pk; advan4; dep_two_cmp; blq

Input Script:

blq_pk_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA]

1.0000

0.2060

0.7940

0.7940

f[CL]

1.0000

1.9987

0.9987

0.9987

f[V1]

20.0000

50.9527

30.9527

1.5476

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE]

0.1000

0.1469

0.0469

0.4694

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA_isv]

0.0500

0.0532

0.0032

0.0632

f[KA_isv;CL_isv]

0.0100

0.0111

0.0011

0.1131

f[KA_isv;V1_isv]

0.0100

-0.0286

0.0386

3.8623

f[CL_isv;KA_isv]

0.0100

0.0111

0.0011

0.1131

f[CL_isv]

0.0500

0.0289

0.0211

0.4222

f[CL_isv;V1_isv]

0.0100

0.0239

0.0139

1.3938

f[V1_isv;KA_isv]

0.0100

-0.0286

0.0386

3.8623

f[V1_isv;CL_isv]

0.0100

0.0239

0.0139

1.3938

f[V1_isv]

0.0500

0.0642

0.0142

0.2830

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-786.5417

which required 1.14 iterations and took 166.96 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.2060
f[CL] = 1.9987
f[V1] = 50.9527
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0532, 0.0111, -0.0286 ],
    [ 0.0111, 0.0289, 0.0239 ],
    [ -0.0286, 0.0239, 0.0642 ],
]
f[PNOISE] = 0.1469
f[ANOISE] = 0.0100

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] = 1.0000
f[CL] = 1.0000
f[V1] = 20.0000
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0500, 0.0100, 0.0100 ],
    [ 0.0100, 0.0500, 0.0100 ],
    [ 0.0100, 0.0100, 0.0500 ],
]
f[PNOISE] = 0.1000
f[ANOISE] = 0.0100
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