Merge pull request #8 from oxford-pharmacoepi/require_funs

require funs

DESCRIPTION

@@ -19,6 +19,7 @@ Imports:
     cli,
     dbplyr,
     dplyr,
+    glue,
     magrittr,
     PatientProfiles,
     rlang,

NAMESPACE

@@ -3,6 +3,11 @@
 export(generateCombinationCohortSet)
 export(getIdentifier)
 export(joinOverlap)
+export(requireAge)
+export(requireDemographics)
+export(requireFutureObservation)
+export(requirePriorObservation)
+export(requireSex)
 export(splitOverlap)
 importFrom(magrittr,"%>%")
 importFrom(rlang,":=")

R/requireDemographics.R

@@ -0,0 +1,242 @@
+
+
+#' Restrict cohort on patient demographics
+#'
+#' @param cohort A cohort table in a cdm reference
+#' @param indexDate Variable in cohort that contains the date to compute the
+#' demographics characteristics on which to restrict on.
+#' @param ageRange A list of minimum and maximum age
+#' @param sex Can be "Both", "Male" or "Female". If one of the latter, only
+#' those with that sex will be included.
+#' @param minPriorObservation A mimimum number of prior observation days in
+#' the database.
+#' @param minFutureObservation A minimum number of future observation days in
+#' the database.
+#'
+#' @return
+#' @export
+#'
+#' @examples
+requireDemographics <- function(cohort,
+                                indexDate = "cohort_start_date",
+                                ageRange = list(c(0, 150)),
+                                sex = c("Both"),
+                                minPriorObservation = 0,
+                                minFutureObservation = 0){
+
+  cohort <-  demographicsFilter(cohort = cohort,
+                     indexDate = indexDate,
+                     ageRange = ageRange,
+                     sex = sex,
+                     minPriorObservation = minPriorObservation,
+                     minFutureObservation = minFutureObservation)
+
+  cohort <- cohort %>%
+    CDMConnector::recordCohortAttrition(reason = "Demographic requirements")
+
+  cohort
+}
+
+#' Restrict cohort on age
+#'
+#' @param cohort A cohort table in a cdm reference
+#' @param indexDate Variable in cohort that contains the date to compute the
+#' demographics characteristics on which to restrict on.
+#' @param ageRange A list of minimum and maximum age
+#'
+#' @return
+#' @export
+#'
+#' @examples
+requireAge <- function(cohort,
+                       indexDate = "cohort_start_date",
+                       ageRange = list(c(0, 150))){
+
+  cohort <-  demographicsFilter(cohort = cohort,
+                                indexDate = indexDate,
+                                ageRange = ageRange,
+                                sex = "Both",
+                                minPriorObservation = 0,
+                                minFutureObservation = 0)
+
+  cohort <- cohort %>%
+    CDMConnector::recordCohortAttrition(reason =
+                                          glue::glue("Age requirement: {ageRange[[1]][1]} to {ageRange[[1]][2]}"))
+
+  cohort
+
+}
+
+#' Restrict cohort on sex
+#'
+#' @param cohort A cohort table in a cdm reference
+#' @param sex Can be "Both", "Male" or "Female". If one of the latter, only
+#' those with that sex will be included.
+#'
+#' @return
+#' @export
+#'
+#' @examples
+requireSex <- function(cohort,
+                       sex = c("Both")){
+
+  cohort <-  demographicsFilter(cohort = cohort,
+                                indexDate = "cohort_start_date",
+                                ageRange = list(c(0, 150)),
+                                sex = sex,
+                                minPriorObservation = 0,
+                                minFutureObservation = 0)
+
+  cohort <- cohort %>%
+    CDMConnector::recordCohortAttrition(reason =
+        glue::glue("Sex requirement: {sex}"))
+
+
+  cohort
+
+}
+
+#' Restrict cohort on prior observation
+#'
+#' @param cohort A cohort table in a cdm reference
+#' @param indexDate Variable in cohort that contains the date to compute the
+#' demographics characteristics on which to restrict on.
+#' @param minPriorObservation A mimimum number of prior observation days in
+#' the database.
+#'
+#' @return
+#' @export
+#'
+#' @examples
+requirePriorObservation <- function(cohort,
+                                    indexDate = "cohort_start_date",
+                                    minPriorObservation = 0){
+
+  cohort <-  demographicsFilter(cohort = cohort,
+                                indexDate = indexDate,
+                                ageRange = list(c(0, 150)),
+                                sex = "Both",
+                                minPriorObservation = minPriorObservation,
+                                minFutureObservation = 0)
+
+  cohort <- cohort %>%
+    CDMConnector::recordCohortAttrition(reason =
+        glue::glue("Prior observation requirement: {minPriorObservation} days"))
+
+  cohort
+
+}
+
+#' Restrict cohort on future observation
+#'
+#' @param cohort A cohort table in a cdm reference
+#' @param indexDate Variable in cohort that contains the date to compute the
+#' demographics characteristics on which to restrict on.
+#' @param minFutureObservation A minimum number of future observation days in
+#' the database.
+#'
+#' @return
+#' @export
+#'
+#' @examples
+requireFutureObservation <- function(cohort,
+                                     indexDate = "cohort_start_date",
+                                     minFutureObservation = 0){
+
+  cohort <-  demographicsFilter(cohort = cohort,
+                                indexDate = indexDate,
+                                ageRange = list(c(0, 150)),
+                                sex = "Both",
+                                minPriorObservation = 0,
+                                minFutureObservation = minFutureObservation)
+
+  cohort <- cohort %>%
+    CDMConnector::recordCohortAttrition(reason =
+    glue::glue("Future observation requirement: {minFutureObservation} days"))
+
+  cohort
+
+}
+
+demographicsFilter <- function(cohort,
+                   indexDate,
+                   ageRange,
+                   sex,
+                   minPriorObservation,
+                   minFutureObservation){
+
+  cdm <- attr(cohort, "cdm_reference")
+
+  # validate inputs
+  if (!isTRUE(inherits(cdm, "cdm_reference"))) {
+    cli::cli_abort("cohort must be part of a cdm reference")
+  }
+  if(!"GeneratedCohortSet" %in% class(cohort) ||
+     !all(c("cohort_definition_id", "subject_id",
+            "cohort_start_date", "cohort_end_date") %in%
+          colnames(cohort))){
+    cli::cli_abort("cohort must be a GeneratedCohortSet")
+  }
+  if(!indexDate %in% colnames(cohort)){
+    cli::cli_abort("indexDate must be a date column in the cohort table")
+  }
+
+  if(!is.list(ageRange)){
+    cli::cli_abort("ageRange must be a list")
+  }
+  if(length(ageRange[[1]]) != 2 ||
+     !is.numeric(ageRange[[1]]) ||
+     !ageRange[[1]][2] >= ageRange[[1]][1] ||
+     !ageRange[[1]][1]>=0){
+    cli::cli_abort("ageRange only contain a vector of length two, with the
+                   second number greater or equal to the first")
+  }
+  if(length(ageRange) != 1){
+    cli::cli_abort("Only a single ageRange is currently supported")
+  }
+  if(!all(sex %in% c("Both", "Male", "Female"))){
+    cli::cli_abort("sex must be Both, Male, or Female")
+  }
+  if(length(sex) != 1){
+    cli::cli_abort("Only a single sex option is currently supported")
+  }
+  if(!is.numeric(minPriorObservation) ||
+     length(minPriorObservation) != 1 ||
+     !minPriorObservation >= 0){
+    cli::cli_abort("minPriorObservation must be a positive number")
+  }
+  if(!is.numeric(minFutureObservation) ||
+     length(minFutureObservation) != 1 ||
+     !minFutureObservation >= 0){
+    cli::cli_abort("minFutureObservation must be a positive number")
+  }
+
+  minAge <- ageRange[[1]][1]
+  maxAge <- ageRange[[1]][2]
+  if(sex == "Both"){
+    sex <- c("Male", "Female")
+  }
+
+  # because the cohort table passed to the function might have extra columns
+  # that would conflict with ones we'll add, we'll take the core table first
+  # join later
+
+  working_cohort <- cohort %>%
+    dplyr::select(c("cohort_definition_id", "subject_id",
+                    "cohort_start_date", "cohort_end_date",
+                    indexDate)) %>%
+    PatientProfiles::addDemographics(indexDate = indexDate) %>%
+    dplyr::filter(.data$age >= .env$minAge,
+                  .data$age <= .env$maxAge,
+                  .data$sex %in% .env$sex,
+                  .data$prior_observation >= .env$minPriorObservation,
+                  .data$future_observation >= .env$minFutureObservation)
+
+  cohort <- cohort %>%
+    dplyr::inner_join(working_cohort  %>%
+                        dplyr::select(c("cohort_definition_id", "subject_id",
+                                        "cohort_start_date", "cohort_end_date")),
+                      by = c("cohort_definition_id", "subject_id",
+                             "cohort_start_date", "cohort_end_date"))
+  cohort
+}

README.Rmd

@@ -22,7 +22,7 @@ knitr::opts_chunk$set(
 [![Lifecycle:Experimental](https://img.shields.io/badge/Lifecycle-Experimental-339999)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
 <!-- badges: end -->
 
-The goal of CohortConstructor is to help on the creation of cohorts in the OMOP Common Data Model.
+The goal of CohortConstructor is to help on the creation and manipulation of cohorts in the OMOP Common Data Model.
 
 ## Installation
 
@@ -33,21 +33,54 @@ You can install the development version of CohortConstructor from [GitHub](https
 devtools::install_github("oxford-pharmacoepi/CohortConstructor")
 ```
 
-## Example
+## Example usage
+
+``` {r}
+library(CDMConnector)
+library(PatientProfiles)
+library(CohortConstructor)
+
+con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
+cdm <- cdm_from_con(con, cdm_schema = "main", 
+                    write_schema = c(prefix = "my_study_", schema = "main"))
+```
+
+### Generating concept based cohorts
+``` {r}
+cdm <- generate_concept_cohort_set(cdm = cdm, 
+                            name = "medications",
+                            concept_set = list("diclofenac" = 1124300,
+                                               "acetaminophen" = 1127433))
+cohortSet(cdm$combinations)
+cohort_count(cdm$medications)
+cohort_attrition(cdm$medications)
+```
+
+### Applying demographic requirements
+``` {r}
+cdm$medications %>% 
+  requireDemographics(ageRange = list(c(40, 65)),
+                      sex = "Female")
+cohortSet(cdm$combinations)
+cohort_count(cdm$medications)
+cohort_attrition(cdm$medications)
+```
+
+
+### Combining cohorts
 
 Generate a combination cohort.
 
 ```{r}
-library(PatientProfiles)
-library(CohortConstructor)
-library(CDMConnector)
+cdm <- generateCombinationCohortSet(cdm = cdm, 
+                                    name = "combinations", 
+                                    targetCohortName = "medications")
+
 
-cdm <- mockPatientProfiles()
-cdm <- generateCombinationCohortSet(cdm = cdm, name = "cohort3", targetCohortName = "cohort2")
 
-cdm$cohort3
+cohortSet(cdm$combinations)
+cohortCount(cdm$combinations)
 
-cohortSet(cdm$cohort3)
 
 cdmDisconnect(cdm)
 ```