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.