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Data of immune checkpoint inhibitors.

Source: R/data_doc.R
demo_.Rd

Demo, drug, adr, link, ind, out, srce, and followup are the main table in Vigibase Extract Case Level data. In a regular workflow, you will work with those tables as R objects (e.g. demo, drug, adr, link, ind, out, srce, followup). These built-in example datasets use an underscore "_" to avoid ambiguity with your own tables (e.g. demo_, drug_, adr_, link_, ind_, out_, srce_, followup_). This is a relational database, which means every table has a primary key variable (e.g., UMCReportId for demo_. Keys will allow joints with other tables The full details on the original structure can be found in "VigiBase Extract Case Level - file description.pdf" in your VigiBase ECL folders. demo_ will typically be your cornerstone table, since it contains one row per report. It is the preferred table to update for drugs and adrs identification before performing disproportionality analyses. These tables are subsets of the original ones, with some of the immune checkpoint inhibitor cases or immune-related adverse events. All data shown in these example data are FAKE, which means you shouldn't consider the counts and computations as accurate. Immune checkpoint inhibitors drugs include "Ipilimumab", "Atezolizumab", "Durvalumab", "Nivolumab", "Pembrolizumab", "Avelumab", "Cemiplimab","REGN 2810", "Tremelimumab". More details on how to use vigibase tables can be found in the vignettes. vignette("basic_workflow"), vignette("descriptive"). To build your own tables, use tb_vigibase(). See vignette("getting_started").

Usage

data(demo_)

drug_

adr_

link_

followup_

ind_

out_

srce_

Format

demo_ is a data.table with 7 variables and 750 rows.

  • UMCReportId Integer. The unique identifier of the case report.

  • AgeGroup Character. The age group of the patient. Correspondence table is path_sub/AgeGroup.parquet.

  • Gender Character. Case gender. path_sub/Gender.parquet

  • DateDatabase Character (not date or numeric!). The date of the latest update of the report in the database.

  • Type Character. The type of report. path_sub/ReportType.parquet

  • Region Character. The world region where the report comes from path_sub/Region.parquet.

  • FirstDateDatabase Character. The date the report was first submitted to the database.

drug_ is a data.table with 10 variables and 3514 rows.

  • UMCReportId Integer. See demo_.

  • Drug_Id Integer. The unique identifier of each drug report.

  • MedicinalProd_Id Integer. The medicinalproduct identifier. See get_atc_code().

  • DrecNo Integer. Drug Record Number, pivotal to identify drugs with get_drecno().

  • Seq1, Seq2 Character. Seq 1 and 2 complement DrecNo, in WHODrug dictionary.

  • Route Character. The route of administration of the drug.

  • Basis Character. The reputation basis of the drug (suspect, concomitant, or interacting). path_sub/RepBasis.parquet

  • Amount Character. The amount of drug administered.

  • AmountU Character. The unit of the amount of drug administered. path_sub/SizeUnit.parquet

  • Frequency Character. The frequency of drug administration.

  • FrequencyU Character. The unit of the frequency of drug administration. path_sub/Frequency.parquet

adr_ is a data.table with 4 variables and 2133 rows.

  • UMCReportId Integer. See demo_.

  • Adr_Id Integer. The unique identifier of each adverse event report.

  • MedDRA_Id Integer. The MedDRA identifier of the adverse event. It is used in get_llt_soc() and get_llt_smq().

  • Outcome Character. The outcome of the adverse event. path_sub/Outcome.parquet

link_ is a data.table with 3 variables and 3514 rows. The version built with tb_vigibase() is slightly different than the original one.

  • Drug_Id and Adr_Id . Integers. Together, they are the key variable of link. See drug_ and adr_.

  • Dechallenge1 and 2 Characters. Dechallenge action and outcome. path_sub/Dechallenge.parquet, path_sub/Dechallenge2.parquet

  • Rechallenge1 and 2 Characters. Rechallenge action and outcome. path_sub/Rechallenge.parquet, path_sub/Rechallenge2.parquet

  • TimeToOnsetMin and Max Numerics. The minimum and maximum time to onset of the adverse event.

  • tto_mean Numeric. The mean time to onset of the adverse event. It is the average of TimeToOnsetMin and Max.

  • range Numeric. The incertitude around tto_mean. See vignette("descriptive").

  • UMCReportId Integer. See demo_.

ind_ is a data.table with 2 variables and 2426 rows.

  • Drug_Id Integer. See drug_.

  • Indication Character. The indication of the drug.

out_ is a data.table with 3 variables and 747 rows.

  • UMCReportId Integer. See demo_.

  • Seriousness Character. The seriousness criteria of the report. path_sub/Seriousness.parquet

  • Serious Character. Whether the case is serious or not ("N" No, "Y" Yes)

srce_ is a data.table with 2 variables and 729 rows.

  • UMCReportId Integer. See demo_.

  • Type Character. The Type of Reporter. path_sub/Notifier.parquet

followup_ is a data.table with 2 variables and 902 rows.

  • UMCReportId Integer. See demo_.

  • ReplacedUMCReportId Integer. Previous version of the case, which is no longer available in demo_.

An object of class data.table (inherits from data.frame) with 3514 rows and 12 columns.

An object of class data.table (inherits from data.frame) with 2133 rows and 4 columns.

An object of class data.table (inherits from data.frame) with 5136 rows and 11 columns.

An object of class data.table (inherits from data.frame) with 902 rows and 2 columns.

An object of class data.table (inherits from data.frame) with 2426 rows and 2 columns.

An object of class data.table (inherits from data.frame) with 747 rows and 3 columns.

An object of class data.table (inherits from data.frame) with 729 rows and 2 columns.

Source

None

References

There is none

Examples

data(demo_)
demo_ |> dplyr::count(AgeGroup)
#>    AgeGroup     n
#>      <char> <int>
#> 1:        2     1
#> 2:        5    43
#> 3:        6   173
#> 4:        7   174
#> 5:        8   108
#> 6:        9   251