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Direct PD Model Simultaneous PK/PD Parameter fit

[Generated automatically as a Fitting summary]

Inputs

Description

Name:direct_pd_simul
Title:Direct PD Model Simultaneous PK/PD Parameter fit
Author:Wright Dose Ltd
Abstract:
A simple direct PD Model, based on the amount of drug in the body. That simultaneously fits PK and PD parameters.
The amount in the central compartment is determined by K, which has been previously estimated for each individual.
The amount in the central compartment influences the rate of removal of a biomarker (KOUT).
Keywords:pd; one compartment model; direct
Input Script:direct_pd_simul_fit.pyml
Input Data:synthetic_data.csv
Diagram:

Initial fixed effect estimates

f[CL] = 5
f[V] = 15
f[BASE] = 500
f[KOUT] = 0.1
f[PK_ANOISE] = 5
f[PD_ANOISE] = 5

Outputs

Final objective value

-208.115820594

which required 5 iterations and took 0.81 seconds

Final fitted fixed effects

f[CL] = 1.9992
f[V] = 51.005
f[BASE] = 799.98
f[KOUT] = 0.030588
f[PK_ANOISE] = 0.49676
f[PD_ANOISE] = 0.25265

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)

Plots

Dense sim plots

Alternatively see All dense_sim graph plots

Comparison

Compare Main f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[CL] 1.99915 5 0.600169 3.00085
f[V] 51.0055 15 2.40037 36.0055
f[BASE] 799.977 500 0.599953 299.977
f[KOUT] 0.0305875 0.1 0.694125 0.0694125

Compare Noise f[X]

Variable Name Fitted Value Starting Value Prop Change Abs Change
f[PK_ANOISE] 0.496756 5 0.900649 4.50324
f[PD_ANOISE] 0.252646 5 0.949471 4.74735

Compare Variance f[X]

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