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Expected Variables Present – PCORnet

Source: R/evp_process_pcornet.R
evp_process_pcornet.Rd

Expected Variables Present – PCORnet

Usage

evp_process_pcornet(
  cohort,
  evp_variable_file = expectedvariablespresent::evp_variable_file_pcornet,
  multi_or_single_site = "single",
  anomaly_or_exploratory = "exploratory",
  output_level = "row",
  age_groups = NULL,
  p_value = 0.9,
  time = FALSE,
  time_span = c("2012-01-01", "2020-01-01"),
  time_period = "year"
)

Arguments

cohort

A dataframe with the cohort of patients for your study. Should include the columns:

  • person_id

  • start_date

  • end_date

  • site

evp_variable_file

CSV file with information about each of the variables that should be examined in the function. contains the following columns:

  • variable a label for the variable captured by the associated codeset

  • default_tbl CDM table where data related to the codeset is found

  • concept_field concept_id field with codes from the associated codeset

  • date_field a date field in the default_tbl that should be used for over time analyses

  • vocabulary_field PCORNET ONLY; field in the default_tbl that defines the vocabulary type of the concept (i.e. dx_type) if this field is used, the codeset should have a vocabulary_id column that defines the appropriate vocabularies for each concept

  • codeset_name the name of the codeset file; DO NOT include the file extension

  • filter_logic a string indicating filter logic that should be applied to achieve the desired variable; optional

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.

output_level

the level of output to use for an AUC computation, exclusive to ms_anom_at; either patient or row – defaults to row

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

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 with patient/row counts & proportions for each concept set listed in evp_concept_file. this output should then be used in the evp_output function to generate an appropriate visualization