# 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 objective value

    460.7314

which required 1.16 iterations and took 1.47 seconds

### 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

### Dense sim plots

Alternatively see All dense_sim graph 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

### Compare Variance f[X]