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Depot One Comp PK with BLQ observations set to LLQ

[Generated automatically as a Fitting summary]

Model Description

Name:

blq_pk_norm_fit

Title:

Depot One Comp PK with BLQ observations set to LLQ

Author:

PoPy for PK/PD

Abstract:

Depot One Comp PK model, with BLQ (below level of quantification)
observations set to LLQ (lower limit of quantification).
Keywords:

tutorial; pk; advan4; dep_two_cmp; blq

Input Script:

blq_pk_norm_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA]

1.0000

5.8108

4.8108

4.8108

f[CL]

1.0000

0.9490

0.0510

0.0510

f[V1]

20.0000

88.6022

68.6022

3.4301

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE]

0.1000

0.2207

0.1207

1.2071

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA_isv]

0.0500

1.3304

1.2804

25.6080

f[KA_isv;CL_isv]

0.0100

-0.0176

0.0276

2.7570

f[KA_isv;V1_isv]

0.0100

0.2359

0.2259

22.5922

f[CL_isv;KA_isv]

0.0100

-0.0176

0.0276

2.7570

f[CL_isv]

0.0500

0.0002

0.0498

0.9953

f[CL_isv;V1_isv]

0.0100

-0.0031

0.0131

1.3114

f[V1_isv;KA_isv]

0.0100

0.2359

0.2259

22.5922

f[V1_isv;CL_isv]

0.0100

-0.0031

0.0131

1.3114

f[V1_isv]

0.0500

0.0444

0.0056

0.1124

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

121897.7219

which required 1.30 iterations and took 74.11 seconds

Fitted f[X] values (after fitting)

f[KA] = 5.8108
f[CL] = 0.9490
f[V1] = 88.6022
f[KA_isv,CL_isv,V1_isv] = [
    [ 1.3304, -0.0176, 0.2359 ],
    [ -0.0176, 0.0002, -0.0031 ],
    [ 0.2359, -0.0031, 0.0444 ],
]
f[PNOISE] = 0.2207
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:

synthetic_data.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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