Create cohorts based on patient demographics

demographicsCohort() creates a cohort table based on patient characteristics. If and when an individual satisfies all the criteria they enter the cohort. When they stop satisfying any of the criteria their cohort entry ends.

Usage

demographicsCohort(
  cdm,
  name,
  ageRange = NULL,
  sex = NULL,
  minPriorObservation = NULL,
  .softValidation = TRUE
)

Arguments

cdm

A cdm reference.

name

Name of the new cohort table created in the cdm object.

ageRange

A list of vectors specifying minimum and maximum age.

sex

Can be "Both", "Male" or "Female".

minPriorObservation

A minimum number of continuous prior observation days in the database.

.softValidation

Whether to perform a soft validation of consistency. If set to FALSE four additional checks will be performed: 1) a check that cohort end date is not before cohort start date, 2) a check that there are no missing values in required columns, 3) a check that cohort duration is all within observation period, and 4) that there are no overlapping cohort entries

Value

A cohort table

Examples

# \donttest{
library(CohortConstructor)

cdm <- mockCohortConstructor()

cohort <-  cdm |>
    demographicsCohort(name = "cohort3", ageRange = c(18,40), sex = "Male")
#> ℹ Building new trimmed cohort
#> Adding demographics information
#> Creating initial cohort
#> Trim sex
#> Trim age
#> ✔ Cohort trimmed

attrition(cohort)
#> # A tibble: 3 × 7
#>   cohort_definition_id number_records number_subjects reason_id reason          
#>                  <int>          <int>           <int>     <int> <chr>           
#> 1                    1             10              10         1 Initial qualify…
#> 2                    1              2               2         2 Sex requirement…
#> 3                    1              1               1         3 Age requirement…
#> # ℹ 2 more variables: excluded_records <int>, excluded_subjects <int>

# Can also create multiple demographic cohorts, and add minimum prior history requirements.

cohort <- cdm |>
    demographicsCohort(name = "cohort4",
    ageRange = list(c(0, 19),c(20, 64),c(65, 150)),
    sex = c("Male", "Female", "Both"),
    minPriorObservation = 365)
#> ℹ Building new trimmed cohort
#> Adding demographics information
#> Creating initial cohort
#> Trim sex
#> Trim age
#> Trim prior observation
#> ✔ Cohort trimmed

attrition(cohort)
#> # A tibble: 36 × 7
#>    cohort_definition_id number_records number_subjects reason_id reason         
#>                   <int>          <int>           <int>     <int> <chr>          
#>  1                    1             10              10         1 Initial qualif…
#>  2                    1             10              10         2 Sex requiremen…
#>  3                    1              4               4         3 Age requiremen…
#>  4                    1              3               3         4 Prior observat…
#>  5                    2             10              10         1 Initial qualif…
#>  6                    2              8               8         2 Sex requiremen…
#>  7                    2              2               2         3 Age requiremen…
#>  8                    2              1               1         4 Prior observat…
#>  9                    3             10              10         1 Initial qualif…
#> 10                    3              2               2         2 Sex requiremen…
#> # ℹ 26 more rows
#> # ℹ 2 more variables: excluded_records <int>, excluded_subjects <int>
# }