# 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]