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Getting started

Source: vignettes/getting_started.Rmd
getting_started.Rmd

Purpose

Have you subscribed to VigiBase Extract Case Level, WHODrug and MedDRA?

Congratulations! You’ve made it half way to explore VigiBase.

Now, you need to process those large text/ascii files into R readable files. This is the purpose of this vignette.

Overview

The tb_* functions (Table Builders) will help you through the process.

tb_* function Usage
tb_vigibase Main tables (demo, drugs, adverse drug reactions), and subsidiary tables
tb_who WHODrug tables
tb_meddra MedDRA tables

Deploy tables

Folder and files

First, UNZIP the files you received from UMC (beware that you also need to unzip sub-folders, inside the main zip file).

That will give you a folder structure like this:

  • /vigibase_month_year (replace with your version)
  • …/main
  • …/who
  • …/sub

Gather your meddra files into a single folder, ideally close to the vigibase_month_year one.

  • /meddra_vx (replace vx)

Although the names of the subfolders can be longer (e.g. “main_sep_1_24”), we strongly suggest you shorten them to main, who, sub for Vigibase, and meddra_vx for MedDRA.

Short names will make your code easier to read and maintain.

Table builders (tb_* functions)

Provide a path to these files on your computer.

We usually create path_* objects in R, then use them in functions. This is optional, you can also pass path directly to tb_* functions.

library(vigicaen)

# change for the correct paths on your computer
path_base   <- "../vigibase_ecl/main" 
path_sub    <- "../vigibase_ecl/sub"
path_who    <- "../vigibase_ecl/who"
path_meddra <- "../meddra"

tb_vigibase(
  path_base = path_base, 
  path_sub  = path_sub
  )

tb_who(
  path_who  = path_who
  )

tb_meddra(
  path_meddra = path_meddra
  )

Deploying table have to be done ONCE per database version.

New parquet files will be created in the same directories as provided to path_*.

The suspectedduplicates table is delivered with subsidiary files, but the tb_vigibase() function will output it in the main directory, under a slightly modified name.

Outputs

The tb_* functions create parquet files. Parquet is an open-source format, supported by Arrow.

Not all tables from each source are created during the process.

  • Main: All tables + subsidiary tables (from sub): demo, drug, adr, link, ind, srce, out, followup, suspdup, AgeGroup, Dechallenge, Dechallenge2, Frequency, Gender, Notifier, Outcome, Rechallenge, Rechallenge2, Region, RepBasis, ReportType, RouteOfAdm, Seriousness, and SizeUnit.

  • WHODrug: All tables: mp, thg, pp, ing, srce, org, ccode, atc, sun, pf, str, prg, prt, unitx. Again, check correspondence in the delivered files from UMC.

  • Meddra: meddra_hierarchy, smq_list, and smq_content.

mp is modified in comparison to the original file, drug names were lowered case and trimed, to be more handy. A few columns have also been removed.

Loading tables into R

You can use the arrow package built-in readers to load the tables, or the dt_parquet() function from this package, which is a shorthand for data.table::as.data.table(read_parquet()).


demo   <- dt_parquet(path_main, "demo")
drug   <- dt_parquet(path_main, "drug")
adr    <- dt_parquet(path_main, "adr")
link   <- dt_parquet(path_main, "link")
# etc.

mp     <- dt_parquet(path_who, "mp")

meddra <- dt_parquet(path_meddra, "meddra_hierarchy")

Parquet files will be loaded IN memory, by default. To change this behavior, remember to set in_memory = FALSE. Be careful that the output format will be different.

Working on a computer with low specifications

The main ressource to store objects in R is the RAM. If your computer don’t have much RAM (e.g., only 16GB), you will not be able to load all Vigibase tables at once in your session.

In this case, you can use the in_memory argument of dt_parquet() to load tables OUT of memory.

demo   <- dt_parquet(path_main, "demo", in_memory = FALSE)

This will allow you to go through most of the data management steps.

At the time of statistical modelling, you might need to load a table and/or use modelling packages that do not rely on RAM (one example: biglm)

Subsetting tables

If you want to work on a specific subset of VigiBase (e.g. older adults, some specific drugs, or adverse drug reaction), you can create a dedicated subset with the tb_subset() function.

Currently, you can subset on

  • Age groups

  • Drug record numbers (DrecNo) and Medicinal product IDs (MedicinalProd_Id)

  • MedDRA codes of adverse drug reactions (MedDRA_Id)

Visit tb_subset() help page for more information.

You will need to collect drug or reaction IDs if you want to subset on these. See vignette("basic_workflow").


sv_selection <-
    c(7, #  65 - 74 years group
      8) #  >= 75 years group

wd_in <- "some/place/on/your/computer/containing/vigibase_ecl/main"

wd_out <- paste0(wd_in, "/", "more_than_65_subset", "/")

tb_subset(wd_in, wd_out,
          subset_var = "age",
          sv_selection = sv_selection)