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Module 3 - Temporal Data Density

Source: R/module_density.R
run_density.Rd

Profiles longitudinal data accrual around the index date: record density per patient per month across clinical domains, and follow-up completeness with a censoring-reason breakdown. Maps to FDA RWE Reliability (data accrual).

Usage

run_density(
  con,
  cdm_schema,
  cohort_table,
  cohort_id,
  obs_window = c(-365, 365),
  sparse_warn_min = 1
)

Arguments

con

A live DBI connection (see cdm_connect()).

cdm_schema

Schema holding the clinical CDM tables (e.g. "mimic_cdm").

cohort_table

Cohort table, schema-qualified.

cohort_id

Integer cohort definition id.

obs_window

Length-2 numeric c(pre, post) in days relative to index. Density is computed for records inside [index + pre, index + post]. Default c(-365, 365).

sparse_warn_min

Minimum total records a domain must have within the window before it is considered present; domains below this flag WARN (likely missing/sparse domain). Default 1 (flag only truly empty domains).

Value

A named list:

density_by_domain

data.frame: domain, months_from_index, n_patients, n_records, records_per_patient

followup_summary

data.frame: one row per censoring_reason with n and median_followup_days (median over that group)

followup_detail

per-subject data.frame: subject_id, effective_end_date, followup_days, censoring_reason

flags

character vector of WARN:/FAIL: messages

Examples

if (requireNamespace("duckdb", quietly = TRUE)) {
  con <- example_cdm()
  res <- run_density(con, cdm_schema = "main", cohort_table = "cohort",
                     cohort_id = 1)
  head(res$density_by_domain)
  res$followup_summary
  cdm_disconnect(con)
}