Compute demographic characteristics at a certain date

Usage

addDemographics(
  x,
  indexDate = "cohort_start_date",
  age = TRUE,
  ageName = "age",
  ageMissingMonth = 1,
  ageMissingDay = 1,
  ageImposeMonth = FALSE,
  ageImposeDay = FALSE,
  ageUnit = "years",
  ageGroup = NULL,
  missingAgeGroupValue = "None",
  sex = TRUE,
  sexName = "sex",
  missingSexValue = "None",
  priorObservation = TRUE,
  priorObservationName = "prior_observation",
  priorObservationType = "days",
  futureObservation = TRUE,
  futureObservationName = "future_observation",
  futureObservationType = "days",
  dateOfBirth = FALSE,
  dateOfBirthName = "date_of_birth",
  name = NULL,
  type = "numeric"
)

Arguments

x: A table containing individuals in a CDM reference.
indexDate: Name of a date column in x, or a single date to use for all rows, used as the reference date.
age: If TRUE, age is calculated relative to indexDate.
ageName: Name of the age column to add.
ageMissingMonth: Month of the year assigned when month of birth is missing.
ageMissingDay: Day of the month assigned when day of birth is missing.
ageImposeMonth: If TRUE, month of birth is treated as missing for all individuals.
ageImposeDay: If TRUE, day of birth is treated as missing for all individuals.
ageUnit: Unit in which to express age: "years", "months", or "days".
ageGroup: If not NULL, a list of age-group vectors.
missingAgeGroupValue: Value to use when age is missing.
sex: If TRUE, sex is identified.
sexName: Name of the sex column to add.
missingSexValue: Value to use when sex is missing.
priorObservation: If TRUE, the time between the start of the current observation period and indexDate is calculated.
priorObservationName: Name of the prior-observation column to add.
priorObservationType: Whether to return a "date" or a number of "days".
futureObservation: If TRUE, the time between indexDate and the end of the current observation period is calculated.
futureObservationName: Name of the future-observation column to add.
futureObservationType: Whether to return a "date" or a number of "days".
dateOfBirth: If TRUE, date of birth is returned.
dateOfBirthName: Name of the date-of-birth column to add.
name: Name of the new table. If NULL, a temporary table is returned.
type: Type of the created column(s). Counts, days, age, and observation durations can be "numeric" or "integer". Flag columns can also be "logical". Field columns can use "auto" to preserve the source type, or can be converted to "numeric", "integer", "logical", or "character".

Value

cohort table with the added demographic information columns.

Examples

# \donttest{
library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addDemographics()
#> # A query:  ?? x 8
#> # Database: DuckDB 1.5.4 [unknown@Linux 6.17.0-1020-azure:R 4.6.1/:memory:]
#>    cohort_definition_id subject_id cohort_start_date cohort_end_date   age sex  
#>                   <int>      <int> <date>            <date>          <dbl> <chr>
#>  1                    3          1 1935-12-31        1936-01-19          1 Fema…
#>  2                    3          2 1916-06-05        1922-10-03          1 Fema…
#>  3                    3          3 1959-01-19        1979-08-18          5 Male 
#>  4                    3          4 1962-11-02        1976-09-12          6 Fema…
#>  5                    3          5 1966-01-15        1976-05-09         11 Male 
#>  6                    3          6 1941-03-12        1946-06-28         12 Fema…
#>  7                    3          7 1977-01-10        1980-01-21          0 Fema…
#>  8                    1          8 1926-07-08        1935-03-14          8 Fema…
#>  9                    2          9 1989-11-29        1992-11-26         24 Male 
#> 10                    3         10 1977-07-25        1983-02-04          4 Fema…
#> # ℹ 2 more variables: prior_observation <dbl>, future_observation <dbl>

cdm$cohort1 |>
  addDemographics(indexDate = as.Date("2010-01-01"))
#> # A query:  ?? x 8
#> # Database: DuckDB 1.5.4 [unknown@Linux 6.17.0-1020-azure:R 4.6.1/:memory:]
#>    cohort_definition_id subject_id cohort_start_date cohort_end_date   age sex  
#>                   <int>      <int> <date>            <date>          <dbl> <chr>
#>  1                    3          1 1935-12-31        1936-01-19         76 Fema…
#>  2                    3          2 1916-06-05        1922-10-03         95 Fema…
#>  3                    3          3 1959-01-19        1979-08-18         56 Male 
#>  4                    3          4 1962-11-02        1976-09-12         54 Fema…
#>  5                    3          5 1966-01-15        1976-05-09         55 Male 
#>  6                    3          6 1941-03-12        1946-06-28         81 Fema…
#>  7                    3          7 1977-01-10        1980-01-21         33 Fema…
#>  8                    1          8 1926-07-08        1935-03-14         92 Fema…
#>  9                    2          9 1989-11-29        1992-11-26         45 Male 
#> 10                    3         10 1977-07-25        1983-02-04         37 Fema…
#> # ℹ 2 more variables: prior_observation <dbl>, future_observation <dbl>

# }