Get missingness rates for columns — ds.omop.missingness • dsOMOPClient

Computes the proportion and count of NULL/missing values for each column (or a subset of columns) in an OMOP CDM table. This is essential for data quality assessment, helping identify columns with high missingness that may affect downstream analyses. Results are disclosure-controlled; columns where the non-null count falls below the threshold are suppressed.

Usage

ds.omop.missingness(
  table,
  columns = NULL,
  cohort = NULL,
  scope = c("per_site", "pooled"),
  pooling_policy = "strict",
  symbol = "omop",
  conns = NULL,
  execute = TRUE
)

Arguments

table: Character; the CDM table name (e.g., "measurement", "observation").
columns: Character vector; specific column names to check, or NULL to check all columns in the table (default: NULL).
cohort: Cohort reference (a dsomop_cohort_handle, a cohort_definition_id, or a server-side cohort table name), or NULL.
scope: Character; "per_site" (default) or "pooled".
pooling_policy: Character; "strict" (default) or "pooled_only_ok".
symbol: Character; the session symbol (default: "omop").
conns: DSI connection object(s) or NULL to use the session default.
execute: Logical; if FALSE, return a dry-run result containing only the generated call code (default: TRUE).

Value

A dsomop_result object with $per_site (named list of data frames with columns column_name, total_count, null_count, missing_pct), $pooled (combined missingness when pooled), and $meta.

Examples

if (FALSE) { # \dontrun{
miss <- ds.omop.missingness("measurement",
                             columns = c("value_as_number",
                                         "value_as_concept_id"))
miss$per_site$server1
} # }