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Patient Event Sequencing – OMOP

Source: R/pes_process_omop.R
pes_process_omop.Rd

Patient Event Sequencing – OMOP

Usage

pes_process_omop(
  cohort,
  user_cutoff = 30,
  n_event_a = 1,
  n_event_b = 1,
  pes_event_file = patienteventsequencing::pes_event_file,
  multi_or_single_site = "single",
  anomaly_or_exploratory = "exploratory",
  age_groups = NULL,
  patient_level_tbl = FALSE,
  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

user_cutoff

user selected number of days between events to be used as a threshold cutoff for analyses

n_event_a

the number of times event A should occur before establishing the index date; defaults to 1

n_event_b

the number of times event B should occur before establishing the occurrence date; defaults to 1

pes_event_file

CSV file with definitions of each of the events

multi_or_single_site

direction to determine what kind of check to run string that is either multi or single

anomaly_or_exploratory

direction to determine what kind of check to run; a string that is either anomaly or exploratory

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 to define whether an intermediate table with patient level output should be returned

p_value

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

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

dataframe with the number of days between events A and B as an integer, and the number of patients who had the events occur that far apart;

    over time analyses will return the same output, grouped by each time
    period in the time span provided