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Concept Set Distribution - OMOP Version

Source: R/csd_process_omop.R
csd_process_omop.Rd

Concept Set Distribution - OMOP Version

Usage

csd_process_omop(
  cohort,
  domain_tbl = conceptsetdistribution::csd_domain_file,
  concept_set = conceptsetdistribution::csd_concept_set,
  multi_or_single_site = "single",
  anomaly_or_exploratory = "exploratory",
  num_concept_combined = FALSE,
  num_concept_1 = 30,
  num_concept_2 = 30,
  p_value = 0.9,
  age_groups = FALSE,
  time = TRUE,
  time_span = c("2012-01-01", "2020-01-01"),
  time_period = "year"
)

Arguments

cohort

cohort for SQUBA testing; required fields:

  • site

  • person_id

  • start_date

  • end_date

domain_tbl

input table defining the domains listed in the annotated concept set four columns: - domain the name of the CDM table associated with the concept; should match what is listed in the annotated concept set - concept_field the name of the field in the domain table where the concepts are located - date_field the name of the field in the domain table with the date that should be used for time-based filtering - vocabulary_field PCORnet only; set to NA

concept_set

an annotated concept set CSV file with the following columns:

  • concept_id required for OMOP; the concept_id of interest

  • concept_code required for PCORnet; the code of interest

  • concept_name optional; the descriptive name of the concept

  • vocabulary_id required for PCORnet; the vocabulary of the code - should match what is listed in the domain table's vocabulary_field

  • variable required; a string label grouping one concept code into a larger variable definition

  • domain required; the name of the CDM table where the concept is stored - multiple domains can be included in the file, but only one domain should be listed per row

multi_or_single_site

Option to run the function on a single vs multiple sites

  • single: run the function for a single site

  • multi: run the function for multiple sites

anomaly_or_exploratory

Option to conduct an exploratory or anomaly detection analysis. Exploratory analyses give a high level summary of the data to examine the fact representation within the cohort. Anomaly detection analyses are specialized to identify outliers within the cohort.

num_concept_combined

when multi_or_single_site = single and anomaly_or_exploratory = anomaly, this argument is a boolean that will ensure that concept1 and concept2 meet some minimal threshold for including in the jaccard index if TRUE, then both conditions for num_concept_1 and num_concept_2 should be met; if FALSE then just one condition needs to be met.

num_concept_1

when multi_or_single_site = single and anomaly_or_exploratory = anomaly, this argument is an integer and requires a minimum number of times that the first concept appears in the dataset

num_concept_2

when multi_or_single_site = single and anomaly_or_exploratory = anomaly, this argument is an integer and requires a minimum number of times that the second concept appears in the dataset

p_value

the p value to be used as a threshold in the multi-site anomaly detection analysis

age_groups

If you would like to stratify the results by age group, create a table or CSV file with the following columns and include it as the age_groups function parameter:

  • min_age: the minimum age for the group (i.e. 10)

  • max_age: the maximum age for the group (i.e. 20)

  • group: a string label for the group (i.e. 10-20, Young Adult, etc.)

If you would not like to stratify by age group, leave the argument as NULL

time

logical to determine whether to output the check across time

time_span

when time = TRUE, a vector of two dates for the observation period of the study

time_period

when time = TRUE, this argument defines the distance between dates within the specified time period. defaults to year, but other time periods such as month or week are also acceptable

Value

a dataframe summarizing the distribution of code usage for each user defined variable