- Language: en
Direct PD Model¶
[Generated automatically as a Fitting summary]
Inputs¶
Description¶
| Name: | direct_pd |
|---|---|
| Title: | Direct PD Model |
| Author: | Wright Dose Ltd |
| Abstract: |
A simple direct PD Model, based on the amount of drug in the body.
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_fit.pyml |
| Input Data: | synthetic_data.csv |
| Diagram: |
Initial fixed effect estimates¶
f[BASE] = 500.0000
f[KOUT] = 0.1000
f[ANOISE] = 5.0000
Outputs¶
Final fitted fixed effects¶
f[BASE] = 800.0443
f[KOUT] = 0.0300
f[ANOISE] = 10.0000
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¶
Comparison¶
Compare Main f[X]¶
| Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
|---|---|---|---|---|
| f[BASE] | 500.0000 | 800.0443 | 0.6001 | 300.0443 |
| f[KOUT] | 0.1000 | 0.0300 | 0.6999 | 0.0700 |
Compare Noise f[X]¶
| Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change |
|---|---|---|---|---|
| f[ANOISE] | 5.0000 | 10.0000 | 1.0000 | 5.0000 |