vachette

A method for visualization of PMx models

https://github.com/certara/vachette

Repository

A method for visualization of PMx models

README.Rmd

---
output: github_document
---

```{r setup, echo=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message=FALSE)
```

# vachette

A method to visualize PKPD analyses which are impacted by 
covariate effects. Variability-Aligned Covariate Harmonized-Effects 
and Time-Transformation Equivalent (VACHETTE) facilitates intuitive 
overlays of data and model predictions, allowing for comprehensive 
comparison without dilution effects. VACHETTE improves upon previous
methods [V2ACHER](https://ascpt.onlinelibrary.wiley.com/doi/10.1002/psp4.12679), enabling its
application to all pharmacometric models and enhancing Visual Predictive
Checks (VPC) by integrating data into cohesive plots that can highlight 
model misspecification.

## Installation

## Development 

```{r, eval = FALSE}
remotes::install_github("certara/vachette")
```

## Released

CRAN release coming soon.

## Usage

For a full overview of `vachette`, visit the following tutorial link: https://certara.shinyapps.io/vachette

### Import Data

The minimal required inputs for vachette are two data.frame objects: the observed (`obs.data`) and simulated predictions of typical curves (`typ.data`).

The user also has the option to include simulated data (`sim.data`), in order to derive transformations needed for a VACHETTE VPC. The `sim.data` requires the same columns as `obs.data`, in addition to a required `REP` column to indicate the replicate number. Learn more about data requirements [here](https://certara.shinyapps.io/vachette/#section-how_it_works).

Data files:

* iv-obs.csv
* iv-typ-minmax.csv

```{r}
obs <- read.csv(system.file(package = "vachette", "examples", "iv-obs.csv"))
typ <- read.csv(system.file(package = "vachette", "examples", "iv-typ-minmax.csv"))
```

### Vachette Transformations

Generate transformations for `obs.data` and `sim.data`

```{r}
library(vachette)

vd <-
  vachette_data(
    obs.data = obs,
    typ.data = typ,
    covariates = c(WT=70),
    mappings = c(x = "time",
                 OBS = "DV"),
    model.name  = "intravenous"
  ) |>
  apply_transformations()

```

### Vachette Plot

Generate `vachette` plots

```{r readme-plots}
p.obs.ref.query(vd) 

p.vachette(vd)

p.scaled.typical.full.curves.landmarks(vd)

p.scaling.factor(vd)

p.scaled.typical.curves(vd)

p.obs.excluded(vd)
```

View excluded observations

```{r}
vd$obs.excluded
```

Owner

GitHub Events

Packages