vigicaen

Visit the package website

The goal of vigicaen is to provide tools to analyze VigiBase Extract Case Level.

VigiBase is the World Health Organization’s (WHO) global pharmacovigilance database of individual case safety reports. It is maintained by the Uppsala Monitoring Centre in Sweden.

This package is NOT supported nor reflects the opinion of the WHO, or the Uppsala Monitoring Centre.

Prerequisites

Users are assumed to be familiar with pharmacovigilance analysis principles. Some useful resources can be found here (English) or here (French).

vigicaen is an R package, so you need to have R installed on your computer, and optionally RStudio.

Use of VigiBase Extract Case Level and the subsequent WHODrug data requires a license from the Uppsala Monitoring Centre.

Use of MedDRA requires a license from MedDRA.

Of note, academic researchers are provided with accommodations for these licenses.

Technical requisites

Vigibase ECL tables are very large, your computer must meet the following requirements:

• Free disk space of at least 50GB

• At least 16GB of RAM

• A not too old processor (partial tests conducted on 2019 Intel 7, and more recent 2023 AMD Ryzen 3)

Target users

There are 2 types of users this package is aimed at:

• Routine pharmacovigilance practitioners. These users may not be very familiar with R, or statistics in general. They would like to collect additional data, when writing pharmacovigilance reports, or working on a reported case (information component, reaction time to onset). These users will be interested in the “Routine pharmacovigilance” vignette, vignette("routine_pharmacovigilance").

• Advanced pharmacovigilance researchers. These users must be familiar with R and (a bit of) statistics. The will find tools to load tables, perform usual data management, identify drug and reaction IDs, describe complexe features (dechallenge, rechallenge), perform disproportionality, and get ready-to-use datasets to apply any regression or machine learning algorithm.

Installation

Solution 1

From CRAN

install.packages("vigicaen")

Development version from GitHub

devtools::install_github("pharmacologie-caen/vigicaen")

Solution 2

Find the latest Released version here

Download source code as a tar.gz file.

If you use RStudio, click on “Tools”, “Install Packages…”, select “Package Archive file” and locate the tar.gz file on your computer.

Alternatively, you can use the following command in R:

install.packages("path/to/vigicaen_X.XX.X.tar.gz", repos = NULL, type = "source")

Cheatsheet

How to use

Visit the package website

Good places to start your journey:

• Set the stage with vignette("getting_started")

• Explore vigibase vignette("routine_pharmacovigilance")

• Dive into advanced features vignette("basic_workflow")

Example

You are working on a colitis case reported 80 days after ipilimumab initiation.

You would like to know the information component (possibly restricted to a specific population, e.g. older adults), and the time to onset reported for this reaction.

library(vigicaen)

# Step 1: Load datasets (or use example sets
# as shown below)

demo   <- demo_
adr    <- adr_
drug   <- drug_
link   <- link_
mp     <- mp_
meddra <- meddra_

# Step 2: Pick a drug and a reaction

d_code <- 
  list(
    ipilimumab = "ipilimumab"
  ) |> 
  get_drecno(mp = mp)

a_code <-
  list(
    colitis = "Colitis (excl infective)"
  ) |> 
  get_llt_soc(term_level = "hlt", meddra = meddra)

# Step 3: Plot results

vigi_routine(
  case_tto  = 80, # your case
  demo_data = demo,
  drug_data = drug,
  adr_data  = adr,
  link_data = link,
  d_code    = d_code,
  a_code    = a_code,
  vigibase_version = "September 2024"
)

Example

You want to perform a disproportionality analysis between nivolumab exposure and colitis reporting (reporting odds-ratio or and information component ic).

library(vigicaen)

demo <-
  demo_ |> 
  add_drug(
    d_code = ex_$d_drecno,
    drug_data = drug_
  ) |> 
  add_adr(
    a_code = ex_$a_llt,
    adr_data = adr_
  )

demo |> 
  compute_dispro(
    y = "a_colitis",
    x = "nivolumab"
  )
#> # A tibble: 1 × 9
#>   y         x         n_obs n_exp or    or_ci          ic ic_tail ci_level
#>   <chr>     <chr>     <dbl> <dbl> <chr> <chr>       <dbl>   <dbl> <chr>   
#> 1 a_colitis nivolumab    44  31.2 1.88  (1.23-2.88) 0.489  0.0314 95%