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Patient Record Consistency – OMOP

Source: R/prc_process_omop.R
prc_process_omop.Rd

Patient Record Consistency – OMOP

Usage

prc_process_omop(
  cohort,
  prc_event_file,
  multi_or_single_site = "single",
  anomaly_or_exploratory = "exploratory",
  age_groups = NULL,
  patient_level_tbl = FALSE,
  fu_breaks = c(0, 1, 3, 8, 11, 15, 25, 50, 100),
  p_value = 0.9,
  time = FALSE,
  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

prc_event_file

a table with the definitions for each event with the columns

  • event - A or B

  • event_label - a descriptive label for the event

  • domain_tbl - the default CDM table from which data is retrieved

  • concept_field - the field in the table where the codes of interest are stored

  • date_field - the date field to be used to establish the index & occurrence dates

  • vocabulary_field - (PCORnet only) The name of the column in the domain table where the vocabulary type is stored

  • codeset_name - the name of the codeset in the specs directory to define the variable of interest

  • filter_logic - a string indicating any filter logic that should be applied to establish the event ex: an Hba1c > 6.5

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.

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

patient_level_tbl

logical controlling whether patient level output is returned or not

fu_breaks

a numeric vector that defines how to group different windows of follow up time

p_value

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

time

a logical that tells the function whether you would like to look at the output over time

time_span

when time = TRUE, this argument defines the start and end dates for the time period of interest. should be formatted as c(start date, end date) in yyyy-mm-dd date format

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 how often two events occur and co-occur within a patient record