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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

2.3463

1.3463

1.3463

f[CL]

1.0000

0.9537

0.0463

0.0463

f[V1]

20.0000

89.0333

69.0333

3.4517

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE]

0.1000

0.2204

0.1204

1.2036

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA_isv]

0.0500

0.3507

0.3007

6.0136

f[KA_isv;CL_isv]

0.0100

-0.0046

0.0146

1.4582

f[KA_isv;V1_isv]

0.0100

0.0480

0.0380

3.8030

f[CL_isv;KA_isv]

0.0100

-0.0046

0.0146

1.4582

f[CL_isv]

0.0500

0.0001

0.0499

0.9986

f[CL_isv;V1_isv]

0.0100

-0.0010

0.0110

1.1020

f[V1_isv;KA_isv]

0.0100

0.0480

0.0380

3.8030

f[V1_isv;CL_isv]

0.0100

-0.0010

0.0110

1.1020

f[V1_isv]

0.0500

0.0440

0.0060

0.1203

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

121901.3471

which required 1.30 iterations and took 859.21 seconds

Fitted f[X] values (after fitting)

f[KA] = 2.3463
f[CL] = 0.9537
f[V1] = 89.0333
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.3507, -0.0046, 0.0480 ],
    [ -0.0046, 0.0001, -0.0010 ],
    [ 0.0480, -0.0010, 0.0440 ],
]
f[PNOISE] = 0.2204
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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