Summarise concept use in patient-level data. Only concepts recorded during observation period are counted.

Usage

summariseConceptIdCounts(
  cdm,
  omopTableName,
  countBy = "record",
  year = lifecycle::deprecated(),
  interval = "overall",
  sex = FALSE,
  ageGroup = NULL,
  inObservation = FALSE,
  sample = NULL,
  dateRange = NULL
)

Arguments

cdm: A cdm object
omopTableName: A character vector of the names of the tables to summarise in the cdm object.
countBy: Either "record" for record-level counts or "person" for person-level counts
year: deprecated
interval: Time interval to stratify by. It can either be "years", "quarters", "months" or "overall".
sex: TRUE or FALSE. If TRUE code use will be summarised by sex.
ageGroup: A list of ageGroup vectors of length two. Code use will be thus summarised by age groups.
inObservation: Logical. If TRUE, the results are stratified to indicate whether each record occurs within an observation period.
sample: Either an integer or a character string. If an integer (n > 0), the function will first sample n distinct person_ids from the person table and then subset the input tables to those subjects. If a character string, it must be the name of a cohort in the cdm; in this case, the input tables are subset to subjects (subject_id) belonging to that cohort. Use NULL to disable subsetting (default value).
dateRange: A vector of two dates defining the desired study period. Only the start_date column of the OMOP table is checked to ensure it falls within this range. If dateRange is NULL, no restriction is applied.

Value

A summarised_result object with results overall and, if specified, by strata.

Examples

# \donttest{
library(OmopSketch)
library(CDMConnector)
library(duckdb)

requireEunomia()
#> ℹ `EUNOMIA_DATA_FOLDER` set to: /tmp/RtmpWD23qk.
#> 
#> Download completed!
con <- dbConnect(duckdb(), eunomiaDir())
#> Creating CDM database /tmp/RtmpWD23qk/GiBleed_5.3.zip
cdm <- cdmFromCon(con = con, cdmSchema = "main", writeSchema = "main")

summariseConceptIdCounts(
  cdm = cdm,
  omopTableName = "condition_occurrence",
  countBy = c("record", "person"),
  sex = TRUE
)
#> # A tibble: 476 × 13
#>    result_id cdm_name group_name group_level          strata_name strata_level
#>        <int> <chr>    <chr>      <chr>                <chr>       <chr>       
#>  1         1 Synthea  omop_table condition_occurrence overall     overall     
#>  2         1 Synthea  omop_table condition_occurrence overall     overall     
#>  3         1 Synthea  omop_table condition_occurrence overall     overall     
#>  4         1 Synthea  omop_table condition_occurrence overall     overall     
#>  5         1 Synthea  omop_table condition_occurrence overall     overall     
#>  6         1 Synthea  omop_table condition_occurrence overall     overall     
#>  7         1 Synthea  omop_table condition_occurrence overall     overall     
#>  8         1 Synthea  omop_table condition_occurrence overall     overall     
#>  9         1 Synthea  omop_table condition_occurrence overall     overall     
#> 10         1 Synthea  omop_table condition_occurrence overall     overall     
#> # ℹ 466 more rows
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> #   estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> #   additional_name <chr>, additional_level <chr>
# }