Direct PD Model¶
[Generated automatically as a Fitting summary]
Model Description¶
| Name: | direct_pd |
|---|---|
| Title: | Direct PD Model |
| Author: | PoPy for PK/PD |
| 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 |
| Diagram: |
Comparison¶
Compare Main f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[BASE] | 500.0000 | 800.0499 | 300.0499 | 0.6001 |
| f[KOUT] | 0.1000 | 0.0300 | 0.0700 | 0.6999 |
Compare Noise f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[ANOISE] | 5.0000 | 9.9922 | 4.9922 | 0.9984 |
Compare Variance f[X]¶
Population simulated (sim) plots¶
|
indOBS_vs_TIME |
Outputs¶
Fitted f[X] values (after fitting)¶
f[BASE] = 800.0499
f[KOUT] = 0.0300
f[ANOISE] = 9.9922
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: | cx_obs_params.csv |
|---|
Starting f[X] values (before fitting)¶
f[BASE] = 500.0000
f[KOUT] = 0.1000
f[ANOISE] = 5.0000