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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

0.2064

0.7936

0.7936

f[CL]

1.0000

1.9975

0.9975

0.9975

f[V1]

20.0000

50.9682

30.9682

1.5484

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE]

0.1000

0.1476

0.0476

0.4758

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA_isv]

0.0500

0.0474

0.0026

0.0516

f[KA_isv;CL_isv]

0.0100

0.0120

0.0020

0.2028

f[KA_isv;V1_isv]

0.0100

-0.0314

0.0414

4.1393

f[CL_isv;KA_isv]

0.0100

0.0120

0.0020

0.2028

f[CL_isv]

0.0500

0.0287

0.0213

0.4254

f[CL_isv;V1_isv]

0.0100

0.0244

0.0144

1.4382

f[V1_isv;KA_isv]

0.0100

-0.0314

0.0414

4.1393

f[V1_isv;CL_isv]

0.0100

0.0244

0.0144

1.4382

f[V1_isv]

0.0500

0.0628

0.0128

0.2555

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

-786.4915

which required 1.18 iterations and took 1421.28 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.2064
f[CL] = 1.9975
f[V1] = 50.9682
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.0474, 0.0120, -0.0314 ],
    [ 0.0120, 0.0287, 0.0244 ],
    [ -0.0314, 0.0244, 0.0628 ],
]
f[PNOISE] = 0.1476
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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