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Missingness

Run missingness check

library(DrugExposureDiagnostics)
library(dplyr)
library(DT)

# acetaminophen concept id is 1125315
acetaminophen <- 1125315
cdm <- mockDrugExposure()
acetaminophen_checks <- executeChecks(cdm = cdm, 
                                      ingredients = acetaminophen, 
                                      checks = "missing")

Overall missingness

This shows the missingness of the drug records summarised on ingredient level.

datatable(acetaminophen_checks$missingValuesOverall,
  rownames = FALSE
)
Column Description
ingredient_concept_id Concept ID of ingredient.
ingredient Name of drug ingredient.
variable the variable for which missingness was assessed.
n_records Number of records for ingredient concept. If n_records is the same as n_sample this means that there are more records but the number was cut at the pre-specified sample number for efficiency reasons.
n_sample The pre-specified maximum sample. If n_records is smaller than the sample it means that sampling was ignored because the total number of records was already too small.
n_records_not_missing_value The number of records for which there is no missingness in the variable of interest.
n_records_missing_value The number of records with missing values for the variable of interest.
proportion_records_missing_value The proportion of records with missing values for the variable of interest.
result_obscured TRUE if count has been suppressed due to being below the minimum cell count, otherwise FALSE.

Missingness by drug concept

This shows the missingness on drug concept level.

datatable(acetaminophen_checks$missingValuesByConcept,
  rownames = FALSE
)
Column Description
drug_concept_id ID of the drug concept.
drug Name of the drug concept.
ingredient_concept_id Concept ID of ingredient.
ingredient Name of drug ingredient.
variable the variable for which missingness was assessed.
n_records Number of records for drug concept. If n_records is the same as n_sample this means that there are more records but the number was cut at the pre-specified sample number for efficiency reasons.
n_sample The pre-specified maximum sample. If n_records is smaller than the sample it means that sampling was ignored because the total number of records was already too small.
n_records_not_missing_value The number of records for which there is no missingness in the variable of interest.
n_records_missing_value The number of records with missing values for the variable of interest.
proportion_records_missing_value The proportion of records with missing values for the variable of interest.
result_obscured TRUE if count has been suppressed due to being below the minimum cell count, otherwise FALSE.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.