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Depot One Comp PK with BLQ observations set to 0.5*LLQ

[Generated automatically as a Fitting summary]

Model Description

Name:

blq_pk_norm_fit_half

Title:

Depot One Comp PK with BLQ observations set to 0.5*LLQ

Author:

PoPy for PK/PD

Abstract:

Depot One Comp PK model, with BLQ (below level of quantification)
observations set to 0.5*LLQ (lower limit of quantification).
Keywords:

tutorial; pk; advan4; dep_two_cmp; blq

Input Script:

blq_pk_norm_fit_half.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA]

1.0000

1.0467

0.0467

0.0467

f[CL]

1.0000

1.6694

0.6694

0.6694

f[V1]

20.0000

81.9258

61.9258

3.0963

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE]

0.1000

0.3225

0.2225

2.2247

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KA_isv]

0.0500

0.2479

0.1979

3.9573

f[KA_isv;CL_isv]

0.0100

0.0438

0.0338

3.3760

f[KA_isv;V1_isv]

0.0100

0.1067

0.0967

9.6689

f[CL_isv;KA_isv]

0.0100

0.0438

0.0338

3.3760

f[CL_isv]

0.0500

0.0077

0.0423

0.8452

f[CL_isv;V1_isv]

0.0100

0.0188

0.0088

0.8834

f[V1_isv;KA_isv]

0.0100

0.1067

0.0967

9.6689

f[V1_isv;CL_isv]

0.0100

0.0188

0.0088

0.8834

f[V1_isv]

0.0500

0.0459

0.0041

0.0814

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

(No population graphs were requested.)

Outputs

Final objective value

28107.5222

which required 1.30 iterations and took 78.22 seconds

Fitted f[X] values (after fitting)

f[KA] = 1.0467
f[CL] = 1.6694
f[V1] = 81.9258
f[KA_isv,CL_isv,V1_isv] = [
    [ 0.2479, 0.0438, 0.1067 ],
    [ 0.0438, 0.0077, 0.0188 ],
    [ 0.1067, 0.0188, 0.0459 ],
]
f[PNOISE] = 0.3225
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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