R/create_analytic_cohort.R
In AxelitoMartin/GenieBPC: Project GENIE BioPharma Collaborative Data Processing Pipeline
Defines functions
create_analytic_cohort
Documented in
create_analytic_cohort
#' Select cohort of patients for analysis
#'
#' This function allows the user to create a cohort from the GENIE BPC data
#' based on cancer diagnosis information such as cancer cohort, treating
#' institution, histology, and stage at diagnosis, as well as cancer-directed
#' regimen information including regimen name and regimen order. This function
#' returns each of the clinical and genomic data files subset on the patients
#' that met criteria for the analytic cohort. Documentation regarding the
#' structure and contents of each file can be found in the Analytic Data Guide
#' corresponding to each data release, as well as in the
#' \href{https://genie-bpc.github.io/genieBPC/articles/clinical_data_structure_vignette.html}{Clinical Data Structure vignette}.
#'
#' See the \href{https://genie-bpc.github.io/genieBPC/articles/create_analytic_cohort_vignette.html}{create_analytic_cohort vignette} for further documentation and examples.
#'
#' @param data_synapse The item from the nested list returned from
#' pull_data_synapse() that corresponds to the cancer cohort of interest.
#' @param index_ca_seq Index cancer sequence. Default is 1, indicating the
#' patient's first index cancer. The index cancer is also referred to as the
#' BPC Project cancer in the GENIE BPC Analytic Data Guide; this is the
#' cancer that met the eligibility criteria for the project and was
#' selected at random for PRISSMM phenomic data curation.
#' Specifying multiple index cancer sequences, e.g.
#' index_ca_seq = c(1, 2) will return index cancers to
#' patients with 1 index cancer and will return the first AND second index
#' cancers to patients with multiple.
#' @param institution GENIE BPC participating institution. Must be one of
#' "DFCI", "MSK", "UHN", or "VICC" for NSCLC, BLADDER, Prostate, and PANC cohorts; must be one of "DFCI",
#' "MSK", "VICC" for CRC and BrCa. Default selection is all institutions.
#' This parameter corresponds to the variable `institution` in the
#' Analytic Data Guide.
#' @param stage_dx Stage at diagnosis. Must be one of "Stage I", "Stage II",
#' "Stage III", "Stage I-III NOS", "Stage IV". The default selection is all
#' stages.
#' Note that if this parameter is specified, any cases that are missing stage
#' information are automatically excluded from the resulting cohort.
#' This parameter corresponds to the variable `stage_dx` in the
#' Analytic Data Guide.
#' @param histology Cancer histology. For all cancer cohorts except for BrCa
#' (breast cancer), this parameter corresponds to the variable
#' `ca_hist_adeno_squamous` and must be one of "Adenocarcinoma",
#' "Squamous cell", "Sarcoma",
#' "Small cell carcinoma", "Carcinoma", "Other histologies/mixed tumor".
#' For BrCa, this parameter corresponds to the variable
#' `ca_hist_brca` and must be one of
#' "Invasive lobular carcinoma", "Invasive ductal carcinoma", "Other histology".
#' The default selection is all histologies. Note that if this parameter is
#' specified, any cases that are missing histology information are automatically
#' excluded from the resulting cohort.
#' @param regimen_drugs Vector with names of drugs in cancer-directed regimen,
#' separated by a comma. For example, to specify a regimen consisting of
#' Carboplatin and Pemetrexed, specify regimen_drugs = "Carboplatin,
#' Pemetrexed". Acceptable values are found in the `drug_regimen_list`
#' dataset provided with this package. This parameter
#' corresponds to the variable `regimen_drugs` in the Analytic Data Guide.
#' @param regimen_type Indicates whether the regimen(s) specified in
#' `regimen_drugs` indicates the exact regimen to return, or if regimens
#' containing the drugs listed in `regimen_drugs` should be returned. Must be
#' one of "Exact" or "Containing". The default is "Exact".
#' @param regimen_order Order of cancer-directed regimen. If multiple drugs
#' are specified, `regimen_order` indicates the regimen order for all drugs;
#' different values of `regimen_order` cannot be specified for different drug
#' regimens. If multiple values are specified, e.g. c(1, 2), then drug regimens
#' that met either order criteria are returned.
#' @param regimen_order_type Specifies whether the `regimen_order` parameter
#' refers to the order of receipt of the drug regimen within the cancer
#' diagnosis (across all other drug regimens; "within cancer") or the order of
#' receipt of the drug regimen within the times that that drug regimen was
#' administered (e.g. the first time carboplatin pemetrexed was received, out
#' of all times that the patient received carboplatin pemetrexed; "within
#' regimen"). Acceptable values are "within cancer" and "within regimen".
#' @param return_summary Specifies whether a summary table for the cohort is
#' returned. Default is FALSE. The `gtsummary` package is required to return a
#' summary table.
#'
#' @return A list of data frames containing clinical and next generation
#' sequencing information for patients that met the specified criteria.
#' Optionally, if return_summary = TRUE, the list also includes summary
#' tables for the number of records per dataset (`tbl_overall_summary`)
#' as well as tables of key cancer diagnosis (`tbl_cohort`),
#' cancer-directed regimen (`tbl_drugs`) and next generation sequencing
#' (`tbl_ngs`) variables.
#'
#' @author Jessica Lavery
#' @export
#'
#' @examples
#' # Examples using package test data
#' # Example 1 ----------------------------------
#' # Create a cohort of all patients with stage IV NSCLC adenocarcinoma and
#' # obtain all of their corresponding clinical and genomic data
#'
#' ex1 <- create_analytic_cohort(
#' data_synapse = genieBPC::nsclc_test_data,
#' stage_dx = "Stage IV",
#' histology = "Adenocarcinoma"
#' )
#'
#' names(ex1)
#'
#' # Example 2 ----------------------------------
#' # Create a cohort of all NSCLC patients who received Cisplatin,
#' # Pemetrexed Disodium or Cisplatin, Etoposide as their first drug regimen
#' # for their first index NSCLC
#'
#' ex2 <- create_analytic_cohort(
#' data_synapse = genieBPC::nsclc_test_data,
#' regimen_drugs = c(
#' "Cisplatin, Pemetrexed Disodium",
#' "Cisplatin, Etoposide"
#' ),
#' regimen_order = 1,
#' regimen_order_type = "within cancer"
#' )
#'
#' # Example 3 ----------------------------------
#' # Create a cohort of all NSCLC patients who received Cisplatin, Pemetrexed
#' # Disodium at any time throughout the course of treatment for their
#' # cancer diagnosis,
#' # but in the event that the patient received the drug multiple times,
#' # only select the first time.
#'
#' ex3 <- create_analytic_cohort(
#' data_synapse = genieBPC::nsclc_test_data,
#' regimen_drugs = c("Cisplatin, Pemetrexed Disodium"),
#' regimen_order = 1,
#' regimen_order_type = "within regimen"
#' )
#'
#' @examplesIf genieBPC::.is_connected_to_genie()
#' # Example 4 ----------------------------------
#' # Using create_analytic_cohort with pull_data_synapse
#' nsclc_2_0 <- pull_data_synapse("NSCLC", version = "v2.0-public")
#'
#' ex4 <- create_analytic_cohort(
#' data_synapse = nsclc_2_0$NSCLC_v2.0,
#' regimen_drugs = c("Cisplatin, Pemetrexed Disodium"),
#' regimen_order = 1,
#' regimen_order_type = "within regimen"
#' )
#'
#' @import
#' dplyr
#' purrr
#' stringr
create_analytic_cohort <- function(data_synapse,
index_ca_seq = 1,
institution,
stage_dx,
histology,
regimen_drugs,
regimen_type = "Exact",
regimen_order,
regimen_order_type,
return_summary = FALSE) {
# check parameters
# cohort object
if (missing(data_synapse)) {
stop("Specify the cohort object from the nested list returned by the
pull_data_synapse() function.")
} else if (is.null(data_synapse)) {
stop("The object specified for data_synapse does not exist.")
}
# check input parameter
# trying to check that the pull_data_synapse object returned is
# specific to the cohort
if (!("pt_char" %in% names(data_synapse))) {
stop("The data_synapse parameter is expecting a single cohort, e.g., data_synapse_obj$NSCLC_v2.0.
Be sure to specify only one cohort at a time, even if there are multiple cohorts
in the data_synapse object.")
}
# if (!(stringr::str_to_upper(cohort) %in% c("NSCLC", "CRC", "BRCA"))) {
# stop("Select from available cancer cohorts:
# NSCLC, CRC, BrCa (not case sensitive)")
# }
# if ( sum(!grepl("^NSCLC$", cohort)>0 , !missing(institution_temp) ,
# !grepl(c("^DFCI$|^MSK$|^VICC$|^UHN$"), institution_temp)>0 ) >0 ){
# get cohort name and how it is capitalized in the data_synapse object
cohort_temp <- pull(
pluck(data_synapse, "pt_char") %>%
distinct(.data$cohort),
"cohort"
)
# alphabetize drugs in regimen to match
# how they are stored in variable
# regimen_drugs
if (!missing(regimen_drugs)) {
regimen_drugs_sorted <- map_chr(
strsplit(regimen_drugs, ","), ~
toString(str_to_lower(str_sort(
(str_trim(.x))
)))
)
}
# index cancer sequence
# get max # index cancers/pt
max_index_ca <- pluck(data_synapse, "ca_dx_index") %>%
group_by(.data$cohort, .data$record_id) %>%
summarize(n_index = n(), .groups = "drop") %>%
summarize(max_n_index = max(.data$n_index))
if (max(index_ca_seq) > max_index_ca) {
stop(paste0(
"There are no patients in the cohort with ", max_index_ca,
" index cancer diagnoses. The maximum number of index cancers to
one patient is ", max_index_ca, "."
))
}
# participating institutions by cohort
if (sum(
!missing(institution),
grepl("^NSCLC$|^PANC$|^BLADDER$|^PROSTATE$", stringr::str_to_upper(cohort_temp)) > 0
) > 1) {
if (sum(!grepl(
c("^DFCI$|^MSK$|^VICC$|^UHN$"),
stringr::str_to_upper(institution)
) > 0) > 0) {
stop("Select from available participating institutions. For NSCLC/PANC/BLADDER/Prostate, the
participating institutions were DFCI, MSK, UHN and VICC.")
}
} else if (sum(!missing(institution), grepl(
"^CRC$|^BRCA$",
stringr::str_to_upper(cohort_temp)
) > 0) > 1) {
if (sum(!grepl(c("^DFCI$|^MSK$|^VICC$"), stringr::str_to_upper(institution))
> 0) > 0) {
stop("Select from available participating institutions. For CRC/BrCa, the
participating institutions were DFCI, MSK and VICC.")
}
}
if (missing(institution) & stringr::str_to_upper(cohort_temp) %in%
stringr::str_to_upper(c("NSCLC", "PANC", "BLADDER", "PROSTATE"))) {
institution_temp <- c("DFCI", "MSK", "UHN", "VICC")
} else if (missing(institution) &
stringr::str_to_upper(cohort_temp) %in% c("CRC", "BRCA")) {
institution_temp <- c("DFCI", "MSK", "VICC")
} else {
institution_temp <- stringr::str_to_upper({{ institution }})
}
# to account for unspecified stage
if (missing(stage_dx)) {
stage_dx_temp <- pull(pluck(data_synapse, "ca_dx_index") %>%
dplyr::distinct(stage_dx), stage_dx)
} else {
stage_dx_temp <- {{ stage_dx }}
}
# stage mis-specified
if (!missing(stage_dx) &&
sum(!grepl(
c("^stage i$|^stage ii$|^stage iii$|
^stage i-iii nos$|^stage iv$"),
stringr::str_to_lower(stage_dx)
) > 0) > 0) {
stop("Select from available stages: Stage I, Stage II, Stage III,
Stage I-III NOS, Stage IV")
}
# to account for unspecified histology
if (missing(histology)) {
if (cohort_temp != "BrCa") {
histology_temp <- pull(pluck(data_synapse, "ca_dx_index") %>%
distinct(.data$ca_hist_adeno_squamous), .data$ca_hist_adeno_squamous)
} else {
histology_temp <- pull(
pluck(data_synapse, "ca_dx_index") %>%
distinct(.data$ca_hist_brca),
"ca_hist_brca"
)
}
} else {
histology_temp <- {{ histology }}
}
# histology mis-specified
if (!missing(histology) &&
cohort_temp != "BrCa" &&
sum(!grepl(
c("^adenocarcinoma$|^squamous cell$|^sarcoma$|^small cell
carcinoma$|^carcinoma$|^other histologies/mixed tumor$"),
stringr::str_to_lower(histology)
) > 0) > 0) {
stop("Select from available histology categories: Adenocarcinoma,
Squamous cell, Sarcoma, Small cell carcinoma, Other histologies/mixed
tumor")
}
if (!missing(histology) &&
cohort_temp == "BrCa" &&
sum(!grepl(
c("^invasive lobular carcinoma$|^invasive ductal carcinoma$|
^Other histology$"),
stringr::str_to_lower(histology)
) > 0) > 0) {
stop("Select from available histology categories: Invasive lobular
carcinoma, Invasive ductal carcinoma, Other histology")
}
### drug regimen parameter checks
# if regimen type is mis-specified
if (!missing(regimen_type) | is.numeric(regimen_type)) {
if (!(stringr::str_to_lower(regimen_type) %in% c("exact", "containing"))) {
stop("For regimen_type select from 'exact' or 'containing'")
}
}
# if regimen_order is not numeric
if (!missing(regimen_order) && !is.numeric(regimen_order)) {
stop("The regimen_order parameter must be a numeric value >=1.")
}
# if regimen_order_type is mis-specified
if (!missing(regimen_order_type) &&
(is.numeric(regimen_order_type) ||
!(stringr::str_to_lower(regimen_order_type) %in% c(
"within cancer",
"within regimen"
)))) {
stop("For regimen_order_type select from 'within cancer' or
'within regimen'")
}
# regimen_order_type needs to be specified if regimen_order is specified
if (missing(regimen_order_type) && !missing(regimen_order)) {
stop("Regimen order type must also be specified. Choose from
'within cancer' or 'within regimen'")
}
# can't only specify regimen_order_type
if (!missing(regimen_order_type) && missing(regimen_order)) {
stop("Numeric order must also be specified in 'regimen_order' argument.")
}
# if regimen_type is specified, regimen_drugs must also be specified
if (!missing(regimen_type) && missing(regimen_drugs)) {
stop("If regimen_type is specified, regimen_drugs must also be specified.")
}
if (missing(regimen_order_type)) {
regimen_order_type <- NULL
}
##############################################################################
# pull cancer cohort #
##############################################################################
# select patients based on cohort, institution, stage at diagnosis,
# histology and cancer number
if (cohort_temp != "BrCa") {
cohort_ca_dx <- pluck(data_synapse, "ca_dx_index") %>%
# re-number index cancer diagnoses
dplyr::group_by(.data$cohort, .data$record_id) %>%
dplyr::mutate(index_ca_seq = 1:n()) %>%
dplyr::ungroup() %>%
# apply filter(s)
dplyr::filter(
stringr::str_to_lower(.data$institution) %in%
stringr::str_to_lower(c(institution_temp)),
stringr::str_to_lower(.data$stage_dx) %in%
stringr::str_to_lower(c(stage_dx_temp)),
stringr::str_to_lower(.data$ca_hist_adeno_squamous) %in%
stringr::str_to_lower(c(histology_temp)),
.data$index_ca_seq %in% c({{ index_ca_seq }})
)
} else {
cohort_ca_dx <- pluck(data_synapse, "ca_dx_index") %>%
# re-number index cancer diagnoses
dplyr::group_by(.data$cohort, .data$record_id) %>%
dplyr::mutate(index_ca_seq = 1:n()) %>%
dplyr::ungroup() %>%
# # apply filter(s)
dplyr::filter(
stringr::str_to_lower(.data$institution) %in%
stringr::str_to_lower(c(institution_temp)),
stringr::str_to_lower(.data$stage_dx) %in%
stringr::str_to_lower(c(stage_dx_temp)),
stringr::str_to_lower(.data$ca_hist_brca) %in%
stringr::str_to_lower(c(histology_temp)),
.data$index_ca_seq %in% c({{ index_ca_seq }})
)
}
# pull drug regimens to those patients
# option 1: all drug regimens to all patients in cohort
# regimen_drugs is not specified, regimen_order is not specified
cohort_ca_drugs <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id", "ca_seq"),
pluck(data_synapse, "ca_drugs"),
by = c("cohort", "record_id", "ca_seq")
) %>%
# create order for drug regimen within cancer and within times the
# drug was received
dplyr::group_by(.data$cohort, .data$record_id, .data$ca_seq) %>%
dplyr::arrange(
.data$cohort, .data$record_id,
.data$ca_seq, .data$regimen_number
) %>%
dplyr::mutate(order_within_cancer = 1:n()) %>%
dplyr::ungroup() %>%
# order drugs w/in regimen, have to account for structure of data which is
# 1 reg:assoc ca dx
# (may have more than one row for a drug regimen even if it's the first time
# that drug regimen was received)
dplyr::left_join(.,
pluck(data_synapse, "ca_drugs") %>%
dplyr::distinct(
.data$record_id, .data$regimen_number,
.data$regimen_drugs
) %>%
dplyr::group_by(.data$record_id, .data$regimen_drugs) %>%
dplyr::arrange(
.data$record_id, .data$regimen_number,
.data$regimen_drugs
) %>%
dplyr::mutate(order_within_regimen = 1:n()) %>%
dplyr::ungroup() %>%
dplyr::select(-"regimen_drugs"),
by = c("record_id", "regimen_number")
) %>%
dplyr::left_join(.,
genieBPC::regimen_abbreviations,
by = c("regimen_drugs")
)
# option 2: all "first line" drug regimens (regimens of a certain number,
# within a cancer diagnosis)
# specific regimen number to all pts in cohort, any regimen name
# regimen_drugs is not specified, regimen_order is specified and
# regimen_type = "within cancer"
if (missing(regimen_drugs) && !missing(regimen_order) &&
stringr::str_to_lower(regimen_order_type) == "within cancer") {
# cohort_ca_drugs <- dplyr::left_join(cohort_ca_dx,
# pluck(data_synapse, paste0("ca_drugs_", cohort_temp)),
# by = c("cohort", "record_id", "institution", "ca_seq")
# ) %>%
# dplyr::filter(.data$order_within_cancer %in% c({{ regimen_order }}))
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::filter(.data$order_within_cancer %in% c({{ regimen_order }}))
# restrict cancer cohort to all patients who got a drug regimen
cohort_ca_dx <- dplyr::inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
dplyr::filter(.data$order_within_cancer %in% c({{ regimen_order }})) %>%
dplyr::select("cohort", "record_id", "institution", "ca_seq"),
by = c(
"cohort", "record_id", "institution", "ca_seq"
)
)
}
# if specific drug regimen is requested; exact regimen
# option 3a: all times that exact drug regimen was received
if (!missing(regimen_drugs) && missing(regimen_order) &&
stringr::str_to_lower(regimen_type) == "exact") {
# identify instances of that drug regimen
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::filter(
str_to_lower(.data$regimen_drugs) %in% c(regimen_drugs_sorted) |
str_to_lower(.data$abbreviation) %in% c(regimen_drugs_sorted) #|
# drug_class %in% c(regimen_drugs_sorted)
)
# restrict cancer cohort to patients on that drug regimen
cohort_ca_dx <- dplyr::inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
dplyr::distinct(
.data$cohort, .data$record_id, .data$institution,
.data$ca_seq
),
by = c("cohort", "record_id", "institution", "ca_seq")
)
}
# option 3b: all times that regimen containing drugs was received
if (!missing(regimen_drugs) && missing(regimen_order) &&
stringr::str_to_lower(regimen_type) == "containing") {
# identify instances of that drug regimen
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::filter(grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$regimen_drugs)
) |
grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$abbreviation)
))
# restrict cancer cohort to patients on that drug regimen
cohort_ca_dx <- dplyr::inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
dplyr::distinct(
.data$cohort, .data$record_id, .data$institution,
.data$ca_seq
),
by = c("cohort", "record_id", "institution", "ca_seq")
)
}
# option 4a: 1st (or other) time that exact regimen was received
if (!missing(regimen_drugs) && !missing(regimen_order) &&
stringr::str_to_lower(regimen_order_type) == "within regimen" &&
stringr::str_to_lower(regimen_type) == "exact") {
# identify instances of that drug regimen
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::filter(str_to_lower(.data$regimen_drugs)
%in% c(regimen_drugs_sorted) |
str_to_lower(.data$abbreviation) %in% c(regimen_drugs_sorted)) %>%
# filter on order of interest (e.g. first, all)
dplyr::filter(.data$order_within_regimen %in% c({{ regimen_order }}))
# restrict cancer cohort to patients on that drug regimen
cohort_ca_dx <- dplyr::inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
distinct(
.data$cohort, .data$record_id, .data$institution,
.data$ca_seq
),
by = c("cohort", "record_id", "institution", "ca_seq")
)
}
# option 4b: 1st (or other) time that regimen containing was received
if (!missing(regimen_drugs) &&
!missing(regimen_order) &&
stringr::str_to_lower(regimen_order_type) == "within regimen" &&
stringr::str_to_lower(regimen_type) == "containing") {
# identify instances of that drug regimen
# have to start with full drugs dataset for 'within regimen',
# otherwise are left with all drug regimens to pts in this cohort
cohort_ca_drugs <- pluck(data_synapse, "ca_drugs") %>%
# add on abbreviations
dplyr::left_join(.,
genieBPC::regimen_abbreviations,
by = c("regimen_drugs")
) %>%
# create new order b/c this is regimen CONTAINING drugs listed
# order drugs w/in regimen, have to account for
# structure of data which is
# 1 reg:assoc ca dx
# (may have more than one row for a drug regimen even
# if it's the first time
# that drug regimen was received)
# have to filter on containing regimens first, then re-number
dplyr::filter(grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$regimen_drugs)
) |
grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$abbreviation)
)) %>%
# now re-number w/in containing regimens
dplyr::left_join(.,
pluck(data_synapse, "ca_drugs") %>%
# add on abbreviations
dplyr::left_join(.,
genieBPC::regimen_abbreviations,
by = c("regimen_drugs")
) %>%
# get regimens containing drugs of interest
dplyr::filter(grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$regimen_drugs)
) |
grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$abbreviation)
)) %>%
# get distinct regimen administrations (since regs
# potentially mapped to multiple ca types)
dplyr::distinct(
.data$record_id, .data$regimen_number,
.data$regimen_drugs
) %>%
# order regimens
dplyr::group_by(.data$record_id) %>%
dplyr::arrange(
.data$record_id, .data$regimen_number,
.data$regimen_drugs
) %>%
dplyr::mutate(
order_within_containing_regimen = 1:n()
) %>%
dplyr::ungroup() %>%
dplyr::select(-"regimen_drugs"),
by = c("record_id", "regimen_number")
) %>%
# filter on order of interest (e.g. first, all)
dplyr::filter(.data$order_within_containing_regimen
%in% c({{ regimen_order }})) %>%
# restrict to patients in the cohort (started with all regimens to all
# patients)
dplyr::inner_join(.,
cohort_ca_dx %>%
dplyr::select("cohort", "record_id", "ca_seq"),
by = c("cohort", "record_id", "ca_seq")
) %>%
# create blank variables (dropped below, not having them is unique to
# regimen_order_type = 'containing')
mutate(
order_within_cancer = as.numeric(NA),
order_within_regimen = as.numeric(NA)
)
# restrict cancer cohort to patients on that drug regimen
cohort_ca_dx <- inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
dplyr::distinct(
.data$cohort, .data$record_id, .data$institution,
.data$ca_seq
),
by = c("cohort", "record_id", "institution", "ca_seq")
)
}
# option 5a: specific drugs within a cancer diagnosis, exact regimen
if (!missing(regimen_drugs) &&
!missing(regimen_order) &&
stringr::str_to_lower(regimen_type) == "exact" &&
stringr::str_to_lower(regimen_order_type) == "within cancer") {
# identify instances of that drug regimen
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::filter(
str_to_lower(.data$regimen_drugs) %in% c(regimen_drugs_sorted) |
str_to_lower(.data$abbreviation) %in% c(regimen_drugs_sorted),
.data$order_within_cancer %in% c({{ regimen_order }})
)
# restrict cancer cohort to patients on that drug regimen
cohort_ca_dx <- dplyr::inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
distinct(
.data$cohort, .data$record_id, .data$institution,
.data$ca_seq
),
by = c("cohort", "record_id", "institution", "ca_seq")
)
}
# option 5b: specific drugs within a cancer diagnosis, regimen containing
if (!missing(regimen_drugs) &&
!missing(regimen_order) &&
stringr::str_to_lower(regimen_type) == "containing" &&
stringr::str_to_lower(regimen_order_type) == "within cancer") {
# identify instances of that drug regimen
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::filter(
grepl(paste(regimen_drugs_sorted,
collapse = "|"
), str_to_lower(.data$regimen_drugs)) |
grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$abbreviation)
),
.data$order_within_cancer %in% c({{ regimen_order }})
)
# restrict cancer cohort to patients on that drug regimen
cohort_ca_dx <- dplyr::inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
dplyr::distinct(
.data$cohort, .data$record_id, .data$institution,
.data$ca_seq
),
by = c("cohort", "record_id", "institution", "ca_seq")
)
}
# for patients meeting the specified criteria, also pull related datasets
# patient characteristics
cohort_pt_char <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id"),
pluck(data_synapse, "pt_char"),
by = c("cohort", "record_id")
)
# non-index cancer
cohort_ca_dx_non_index <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id"),
pluck(data_synapse, "ca_dx_non_index"),
by = c("cohort", "record_id")
)
# PRISSMM Path
cohort_prissmm_pathology <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id"),
pluck(data_synapse, "prissmm_pathology"),
by = c("cohort", "record_id")
)
# PRISSMM Imaging
cohort_prissmm_imaging <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id"),
pluck(data_synapse, "prissmm_imaging"),
by = c("cohort", "record_id")
)
# PRISSMM Med Onc
cohort_prissmm_md <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id"),
pluck(data_synapse, "prissmm_md"),
by = c("cohort", "record_id")
)
# TM (if applicable)
if (!is.null(pluck(data_synapse, "tumor_marker"))) {
cohort_tumor_marker <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id"),
pluck(data_synapse, "tumor_marker"),
by = c("cohort", "record_id")
)
}
# RT (if applicable)
if (!is.null(pluck(data_synapse, "ca_radtx"))) {
cohort_ca_radtx <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id", "ca_seq"),
pluck(data_synapse, "ca_radtx"),
by = c("cohort", "record_id", "ca_seq")
)
}
# cancer panel test information
# keep records based on record_id + cancer sequence of interest
cohort_ngs <- dplyr::inner_join(
cohort_ca_dx %>%
dplyr::select("cohort", "record_id", "ca_seq"),
pluck(data_synapse, "cpt"),
by = c("cohort", "record_id", "ca_seq")
) %>%
distinct()
if(any(names(cohort_ngs) == "cpt_sample_type") &
!any(names(cohort_ngs) == "sample_type")){
cohort_ngs <- cohort_ngs %>%
dplyr::mutate(sample_type = case_when(
str_to_lower(.data$cpt_sample_type)
%in% c("1", "primary", "primary tumor") ~ "Primary tumor",
str_to_lower(.data$cpt_sample_type)
%in% c("2", "lymph node metastasis") ~ "Lymph node metastasis",
str_to_lower(.data$cpt_sample_type)
%in% c("3", "distant organ metastasis") ~ "Distant organ metastasis",
str_to_lower(.data$cpt_sample_type)
%in% c("4", "metastasis site unspecified", "metastatic recurrence") ~
"Metastasis site unspecified",
str_to_lower(.data$cpt_sample_type)
%in% c("5", "local recurrence") ~ "Local recurrence",
str_to_lower(.data$cpt_sample_type)
%in% c("6", "unspecified") ~ as.character(NA),
str_to_lower(.data$cpt_sample_type)
%in% c("7", "not applicable or hematologic malignancy") ~
"Not applicable or hematologic malignancy"
))
}
# genomic sequencing information
if (!is.null(pluck(data_synapse, "fusions"))) {
cohort_fusions <- dplyr::inner_join(pluck(data_synapse, "fusions"),
cohort_ngs %>%
dplyr::select("cohort", "cpt_genie_sample_id"),
by = c("Tumor_Sample_Barcode" = "cpt_genie_sample_id")
)
}
if (!is.null(pluck(data_synapse, "mutations_extended"))) {
cohort_mutations_extended <- dplyr::inner_join(pluck(data_synapse,
"mutations_extended"),
cohort_ngs %>%
dplyr::select("cohort", "cpt_genie_sample_id"),
by = c("Tumor_Sample_Barcode" = "cpt_genie_sample_id")
)
}
# cna file is 1 col / tumor sample barcode
if (!is.null(pluck(data_synapse, "cna"))) {
# get list of IDs to keep
cpt_barcode_keep <- pluck(data_synapse, "cpt") %>%
mutate(
Tumor_Sample_Barcode =
stringr::str_replace_all(.data$cpt_genie_sample_id,
pattern = "-",
replacement = "\\."
)
) %>%
pull("Tumor_Sample_Barcode")
cohort_cna <- pluck(data_synapse, "cna") %>%
select("Hugo_Symbol", any_of(cpt_barcode_keep))
}
# if 0 patients are returned
if (nrow(cohort_ca_dx) == 0) {
message("No patients meeting the specified criteria were returned.
Ensure that all parameters were correctly specified. Specifically,
the list of acceptable drugs can be found in the
`drug_regimen_list` dataset available with this package.")
}
# return a table 1 to describe the cancer cohort if the user specifies
if (nrow(cohort_ca_dx) > 0 && return_summary == TRUE) {
# number of records per patient in the diagnosis dataset
n_rec_dx_dset <- cohort_ca_dx %>%
dplyr::group_by(.data$record_id) %>%
dplyr::summarize(n_rec_pt = n(), .groups = "drop") %>%
gtsummary::tbl_summary(
include = "n_rec_pt",
label = n_rec_pt ~ "Number of diagnoses per patient in cohort_ca_dx
data frame",
type = n_rec_pt ~ "categorical"
) %>%
gtsummary::modify_header(
update = list(
stat_0 ~ "**N = {N} patients**"
),
quiet = TRUE
)
n_rec_drugs_dset <- cohort_ca_drugs %>%
dplyr::group_by(.data$record_id) %>%
dplyr::summarize(n_rec_pt = n(), .groups = "drop") %>%
gtsummary::tbl_summary(
include = "n_rec_pt",
label = n_rec_pt ~ "Number of regimens per patient in cohort_ca_drugs
data frame",
type = n_rec_pt ~ "categorical"
)
n_rec_cpt_dset <- cohort_ngs %>%
dplyr::group_by(.data$record_id) %>%
dplyr::summarize(n_rec_pt = n(), .groups = "drop") %>%
gtsummary::tbl_summary(
include = "n_rec_pt",
label = n_rec_pt ~ "Number of CPTs per patient in cohort_ngs
data frame",
type = n_rec_pt ~ "categorical"
)
tbl_overall_summary <- gtsummary::tbl_stack(
tbls = list(
n_rec_dx_dset,
n_rec_drugs_dset,
n_rec_cpt_dset
),
quiet = TRUE
) %>%
gtsummary::bold_labels()
if (cohort_temp != "BrCa") {
tbl_cohort <- cohort_ca_dx %>%
gtsummary::tbl_summary(
include = c(
"cohort", "institution",
"stage_dx", "ca_hist_adeno_squamous"
),
label = list(
cohort ~ "Cohort (cohort)",
institution ~ "Institution (institution)",
stage_dx ~ "Stage at diagnosis (stage_dx)",
ca_hist_adeno_squamous ~ "Histology (ca_hist_adeno_squamous)"
)
) %>%
gtsummary::bold_labels() %>%
gtsummary::modify_header(
update = list(
stat_0 ~ "**N = {N} Diagnoses**"
),
quiet = TRUE
)
} else {
tbl_cohort <- cohort_ca_dx %>%
# dplyr::group_by(.data$record_id) %>%
# dplyr::mutate(n_rec_pt = n()) %>%
# dplyr::ungroup() %>%
gtsummary::tbl_summary(
include = c("cohort", "institution", "stage_dx",
"ca_hist_brca"),
label = list(
cohort ~ "Cohort (cohort)",
institution ~ "Institution (institution)",
stage_dx ~ "Stage at diagnosis (stage_dx)",
ca_hist_brca ~ "Histology (ca_hist_brca)"
)
) %>%
gtsummary::bold_labels() %>%
gtsummary::modify_header(
update = list(
stat_0 ~ "**N = {N} Diagnoses**"
),
quiet = TRUE
)
}
tbl_drugs <- cohort_ca_drugs %>%
gtsummary::tbl_summary(
include = c("cohort", "institution", "regimen_drugs"),
label = list(
cohort ~ "Cohort (cohort)",
institution ~ "Institution (institution)",
regimen_drugs ~ "Drugs in regimen (regimen_drugs)"
)
) %>%
gtsummary::bold_labels() %>%
gtsummary::modify_header(
update = list(
stat_0 ~ "**N = {N} Regimens**"
),
quiet = TRUE
)
tbl_ngs <- cohort_ngs %>%
gtsummary::tbl_summary(
include = c("cohort", "institution",
"cpt_oncotree_code", "cpt_seq_assay_id"),
label = list(
cohort ~ "Cohort (cohort)",
institution ~ "Institution (institution)",
cpt_oncotree_code ~ "OncoTree code (cpt_oncotree_code)",
cpt_seq_assay_id ~ "Sequence assay ID (cpt_seq_assay_id)"
)
) %>%
gtsummary::bold_labels() %>%
gtsummary::modify_header(
update = list(
stat_0 ~ "**N = {N} Cancer Panel Tests**"
),
quiet = TRUE
)
}
# drop variable before returning data frame
cohort_ca_dx <- cohort_ca_dx %>% select(-"index_ca_seq")
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::select(-"order_within_cancer",
-"order_within_regimen",
-"abbreviation")
# order of dataframes, should they exist
df_order <- c(
"cohort_pt_char", "cohort_ca_dx",
"cohort_ca_dx_non_index",
"cohort_ca_radtx", "cohort_ca_drugs",
"cohort_prissmm_imaging", "cohort_prissmm_pathology",
"cohort_prissmm_md", "cohort_tumor_marker",
"cohort_ngs",
"cohort_mutations_extended", "cohort_fusions", "cohort_cna",
"tbl_overall_summary", "tbl_cohort", "tbl_drugs", "tbl_ngs"
)
# return data frames & tables that are present in the function's environment
rtn <- mget(ls(environment(), pattern = "^cohort_|^tbl"),
envir = environment()
)
# save elements on list in order that we want (clinical datasets, genomic
# datasets, tables) and drop any items that don't appear in this run of
# create_analytic_cohort
rtn_ordered <- rtn[c(df_order)] %>%
purrr::compact()
if (nrow(cohort_ca_dx) > 0) {
return(rtn_ordered)
}
} # end of function
AxelitoMartin/GenieBPC documentation built on April 20, 2024, 6:38 a.m.
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Defines functions create_analytic_cohort
Documented in create_analytic_cohort
#' Select cohort of patients for analysis
#'
#' This function allows the user to create a cohort from the GENIE BPC data
#' based on cancer diagnosis information such as cancer cohort, treating
#' institution, histology, and stage at diagnosis, as well as cancer-directed
#' regimen information including regimen name and regimen order. This function
#' returns each of the clinical and genomic data files subset on the patients
#' that met criteria for the analytic cohort. Documentation regarding the
#' structure and contents of each file can be found in the Analytic Data Guide
#' corresponding to each data release, as well as in the
#' \href{https://genie-bpc.github.io/genieBPC/articles/clinical_data_structure_vignette.html}{Clinical Data Structure vignette}.
#'
#' See the \href{https://genie-bpc.github.io/genieBPC/articles/create_analytic_cohort_vignette.html}{create_analytic_cohort vignette} for further documentation and examples.
#'
#' @param data_synapse The item from the nested list returned from
#' pull_data_synapse() that corresponds to the cancer cohort of interest.
#' @param index_ca_seq Index cancer sequence. Default is 1, indicating the
#' patient's first index cancer. The index cancer is also referred to as the
#' BPC Project cancer in the GENIE BPC Analytic Data Guide; this is the
#' cancer that met the eligibility criteria for the project and was
#' selected at random for PRISSMM phenomic data curation.
#' Specifying multiple index cancer sequences, e.g.
#' index_ca_seq = c(1, 2) will return index cancers to
#' patients with 1 index cancer and will return the first AND second index
#' cancers to patients with multiple.
#' @param institution GENIE BPC participating institution. Must be one of
#' "DFCI", "MSK", "UHN", or "VICC" for NSCLC, BLADDER, Prostate, and PANC cohorts; must be one of "DFCI",
#' "MSK", "VICC" for CRC and BrCa. Default selection is all institutions.
#' This parameter corresponds to the variable `institution` in the
#' Analytic Data Guide.
#' @param stage_dx Stage at diagnosis. Must be one of "Stage I", "Stage II",
#' "Stage III", "Stage I-III NOS", "Stage IV". The default selection is all
#' stages.
#' Note that if this parameter is specified, any cases that are missing stage
#' information are automatically excluded from the resulting cohort.
#' This parameter corresponds to the variable `stage_dx` in the
#' Analytic Data Guide.
#' @param histology Cancer histology. For all cancer cohorts except for BrCa
#' (breast cancer), this parameter corresponds to the variable
#' `ca_hist_adeno_squamous` and must be one of "Adenocarcinoma",
#' "Squamous cell", "Sarcoma",
#' "Small cell carcinoma", "Carcinoma", "Other histologies/mixed tumor".
#' For BrCa, this parameter corresponds to the variable
#' `ca_hist_brca` and must be one of
#' "Invasive lobular carcinoma", "Invasive ductal carcinoma", "Other histology".
#' The default selection is all histologies. Note that if this parameter is
#' specified, any cases that are missing histology information are automatically
#' excluded from the resulting cohort.
#' @param regimen_drugs Vector with names of drugs in cancer-directed regimen,
#' separated by a comma. For example, to specify a regimen consisting of
#' Carboplatin and Pemetrexed, specify regimen_drugs = "Carboplatin,
#' Pemetrexed". Acceptable values are found in the `drug_regimen_list`
#' dataset provided with this package. This parameter
#' corresponds to the variable `regimen_drugs` in the Analytic Data Guide.
#' @param regimen_type Indicates whether the regimen(s) specified in
#' `regimen_drugs` indicates the exact regimen to return, or if regimens
#' containing the drugs listed in `regimen_drugs` should be returned. Must be
#' one of "Exact" or "Containing". The default is "Exact".
#' @param regimen_order Order of cancer-directed regimen. If multiple drugs
#' are specified, `regimen_order` indicates the regimen order for all drugs;
#' different values of `regimen_order` cannot be specified for different drug
#' regimens. If multiple values are specified, e.g. c(1, 2), then drug regimens
#' that met either order criteria are returned.
#' @param regimen_order_type Specifies whether the `regimen_order` parameter
#' refers to the order of receipt of the drug regimen within the cancer
#' diagnosis (across all other drug regimens; "within cancer") or the order of
#' receipt of the drug regimen within the times that that drug regimen was
#' administered (e.g. the first time carboplatin pemetrexed was received, out
#' of all times that the patient received carboplatin pemetrexed; "within
#' regimen"). Acceptable values are "within cancer" and "within regimen".
#' @param return_summary Specifies whether a summary table for the cohort is
#' returned. Default is FALSE. The `gtsummary` package is required to return a
#' summary table.
#'
#' @return A list of data frames containing clinical and next generation
#' sequencing information for patients that met the specified criteria.
#' Optionally, if return_summary = TRUE, the list also includes summary
#' tables for the number of records per dataset (`tbl_overall_summary`)
#' as well as tables of key cancer diagnosis (`tbl_cohort`),
#' cancer-directed regimen (`tbl_drugs`) and next generation sequencing
#' (`tbl_ngs`) variables.
#'
#' @author Jessica Lavery
#' @export
#'
#' @examples
#' # Examples using package test data
#' # Example 1 ----------------------------------
#' # Create a cohort of all patients with stage IV NSCLC adenocarcinoma and
#' # obtain all of their corresponding clinical and genomic data
#'
#' ex1 <- create_analytic_cohort(
#' data_synapse = genieBPC::nsclc_test_data,
#' stage_dx = "Stage IV",
#' histology = "Adenocarcinoma"
#' )
#'
#' names(ex1)
#'
#' # Example 2 ----------------------------------
#' # Create a cohort of all NSCLC patients who received Cisplatin,
#' # Pemetrexed Disodium or Cisplatin, Etoposide as their first drug regimen
#' # for their first index NSCLC
#'
#' ex2 <- create_analytic_cohort(
#' data_synapse = genieBPC::nsclc_test_data,
#' regimen_drugs = c(
#' "Cisplatin, Pemetrexed Disodium",
#' "Cisplatin, Etoposide"
#' ),
#' regimen_order = 1,
#' regimen_order_type = "within cancer"
#' )
#'
#' # Example 3 ----------------------------------
#' # Create a cohort of all NSCLC patients who received Cisplatin, Pemetrexed
#' # Disodium at any time throughout the course of treatment for their
#' # cancer diagnosis,
#' # but in the event that the patient received the drug multiple times,
#' # only select the first time.
#'
#' ex3 <- create_analytic_cohort(
#' data_synapse = genieBPC::nsclc_test_data,
#' regimen_drugs = c("Cisplatin, Pemetrexed Disodium"),
#' regimen_order = 1,
#' regimen_order_type = "within regimen"
#' )
#'
#' @examplesIf genieBPC::.is_connected_to_genie()
#' # Example 4 ----------------------------------
#' # Using create_analytic_cohort with pull_data_synapse
#' nsclc_2_0 <- pull_data_synapse("NSCLC", version = "v2.0-public")
#'
#' ex4 <- create_analytic_cohort(
#' data_synapse = nsclc_2_0$NSCLC_v2.0,
#' regimen_drugs = c("Cisplatin, Pemetrexed Disodium"),
#' regimen_order = 1,
#' regimen_order_type = "within regimen"
#' )
#'
#' @import
#' dplyr
#' purrr
#' stringr
create_analytic_cohort <- function(data_synapse,
index_ca_seq = 1,
institution,
stage_dx,
histology,
regimen_drugs,
regimen_type = "Exact",
regimen_order,
regimen_order_type,
return_summary = FALSE) {
# check parameters
# cohort object
if (missing(data_synapse)) {
stop("Specify the cohort object from the nested list returned by the
pull_data_synapse() function.")
} else if (is.null(data_synapse)) {
stop("The object specified for data_synapse does not exist.")
}
# check input parameter
# trying to check that the pull_data_synapse object returned is
# specific to the cohort
if (!("pt_char" %in% names(data_synapse))) {
stop("The data_synapse parameter is expecting a single cohort, e.g., data_synapse_obj$NSCLC_v2.0.
Be sure to specify only one cohort at a time, even if there are multiple cohorts
in the data_synapse object.")
}
# if (!(stringr::str_to_upper(cohort) %in% c("NSCLC", "CRC", "BRCA"))) {
# stop("Select from available cancer cohorts:
# NSCLC, CRC, BrCa (not case sensitive)")
# }
# if ( sum(!grepl("^NSCLC$", cohort)>0 , !missing(institution_temp) ,
# !grepl(c("^DFCI$|^MSK$|^VICC$|^UHN$"), institution_temp)>0 ) >0 ){
# get cohort name and how it is capitalized in the data_synapse object
cohort_temp <- pull(
pluck(data_synapse, "pt_char") %>%
distinct(.data$cohort),
"cohort"
)
# alphabetize drugs in regimen to match
# how they are stored in variable
# regimen_drugs
if (!missing(regimen_drugs)) {
regimen_drugs_sorted <- map_chr(
strsplit(regimen_drugs, ","), ~
toString(str_to_lower(str_sort(
(str_trim(.x))
)))
)
}
# index cancer sequence
# get max # index cancers/pt
max_index_ca <- pluck(data_synapse, "ca_dx_index") %>%
group_by(.data$cohort, .data$record_id) %>%
summarize(n_index = n(), .groups = "drop") %>%
summarize(max_n_index = max(.data$n_index))
if (max(index_ca_seq) > max_index_ca) {
stop(paste0(
"There are no patients in the cohort with ", max_index_ca,
" index cancer diagnoses. The maximum number of index cancers to
one patient is ", max_index_ca, "."
))
}
# participating institutions by cohort
if (sum(
!missing(institution),
grepl("^NSCLC$|^PANC$|^BLADDER$|^PROSTATE$", stringr::str_to_upper(cohort_temp)) > 0
) > 1) {
if (sum(!grepl(
c("^DFCI$|^MSK$|^VICC$|^UHN$"),
stringr::str_to_upper(institution)
) > 0) > 0) {
stop("Select from available participating institutions. For NSCLC/PANC/BLADDER/Prostate, the
participating institutions were DFCI, MSK, UHN and VICC.")
}
} else if (sum(!missing(institution), grepl(
"^CRC$|^BRCA$",
stringr::str_to_upper(cohort_temp)
) > 0) > 1) {
if (sum(!grepl(c("^DFCI$|^MSK$|^VICC$"), stringr::str_to_upper(institution))
> 0) > 0) {
stop("Select from available participating institutions. For CRC/BrCa, the
participating institutions were DFCI, MSK and VICC.")
}
}
if (missing(institution) & stringr::str_to_upper(cohort_temp) %in%
stringr::str_to_upper(c("NSCLC", "PANC", "BLADDER", "PROSTATE"))) {
institution_temp <- c("DFCI", "MSK", "UHN", "VICC")
} else if (missing(institution) &
stringr::str_to_upper(cohort_temp) %in% c("CRC", "BRCA")) {
institution_temp <- c("DFCI", "MSK", "VICC")
} else {
institution_temp <- stringr::str_to_upper({{ institution }})
}
# to account for unspecified stage
if (missing(stage_dx)) {
stage_dx_temp <- pull(pluck(data_synapse, "ca_dx_index") %>%
dplyr::distinct(stage_dx), stage_dx)
} else {
stage_dx_temp <- {{ stage_dx }}
}
# stage mis-specified
if (!missing(stage_dx) &&
sum(!grepl(
c("^stage i$|^stage ii$|^stage iii$|
^stage i-iii nos$|^stage iv$"),
stringr::str_to_lower(stage_dx)
) > 0) > 0) {
stop("Select from available stages: Stage I, Stage II, Stage III,
Stage I-III NOS, Stage IV")
}
# to account for unspecified histology
if (missing(histology)) {
if (cohort_temp != "BrCa") {
histology_temp <- pull(pluck(data_synapse, "ca_dx_index") %>%
distinct(.data$ca_hist_adeno_squamous), .data$ca_hist_adeno_squamous)
} else {
histology_temp <- pull(
pluck(data_synapse, "ca_dx_index") %>%
distinct(.data$ca_hist_brca),
"ca_hist_brca"
)
}
} else {
histology_temp <- {{ histology }}
}
# histology mis-specified
if (!missing(histology) &&
cohort_temp != "BrCa" &&
sum(!grepl(
c("^adenocarcinoma$|^squamous cell$|^sarcoma$|^small cell
carcinoma$|^carcinoma$|^other histologies/mixed tumor$"),
stringr::str_to_lower(histology)
) > 0) > 0) {
stop("Select from available histology categories: Adenocarcinoma,
Squamous cell, Sarcoma, Small cell carcinoma, Other histologies/mixed
tumor")
}
if (!missing(histology) &&
cohort_temp == "BrCa" &&
sum(!grepl(
c("^invasive lobular carcinoma$|^invasive ductal carcinoma$|
^Other histology$"),
stringr::str_to_lower(histology)
) > 0) > 0) {
stop("Select from available histology categories: Invasive lobular
carcinoma, Invasive ductal carcinoma, Other histology")
}
### drug regimen parameter checks
# if regimen type is mis-specified
if (!missing(regimen_type) | is.numeric(regimen_type)) {
if (!(stringr::str_to_lower(regimen_type) %in% c("exact", "containing"))) {
stop("For regimen_type select from 'exact' or 'containing'")
}
}
# if regimen_order is not numeric
if (!missing(regimen_order) && !is.numeric(regimen_order)) {
stop("The regimen_order parameter must be a numeric value >=1.")
}
# if regimen_order_type is mis-specified
if (!missing(regimen_order_type) &&
(is.numeric(regimen_order_type) ||
!(stringr::str_to_lower(regimen_order_type) %in% c(
"within cancer",
"within regimen"
)))) {
stop("For regimen_order_type select from 'within cancer' or
'within regimen'")
}
# regimen_order_type needs to be specified if regimen_order is specified
if (missing(regimen_order_type) && !missing(regimen_order)) {
stop("Regimen order type must also be specified. Choose from
'within cancer' or 'within regimen'")
}
# can't only specify regimen_order_type
if (!missing(regimen_order_type) && missing(regimen_order)) {
stop("Numeric order must also be specified in 'regimen_order' argument.")
}
# if regimen_type is specified, regimen_drugs must also be specified
if (!missing(regimen_type) && missing(regimen_drugs)) {
stop("If regimen_type is specified, regimen_drugs must also be specified.")
}
if (missing(regimen_order_type)) {
regimen_order_type <- NULL
}
##############################################################################
# pull cancer cohort #
##############################################################################
# select patients based on cohort, institution, stage at diagnosis,
# histology and cancer number
if (cohort_temp != "BrCa") {
cohort_ca_dx <- pluck(data_synapse, "ca_dx_index") %>%
# re-number index cancer diagnoses
dplyr::group_by(.data$cohort, .data$record_id) %>%
dplyr::mutate(index_ca_seq = 1:n()) %>%
dplyr::ungroup() %>%
# apply filter(s)
dplyr::filter(
stringr::str_to_lower(.data$institution) %in%
stringr::str_to_lower(c(institution_temp)),
stringr::str_to_lower(.data$stage_dx) %in%
stringr::str_to_lower(c(stage_dx_temp)),
stringr::str_to_lower(.data$ca_hist_adeno_squamous) %in%
stringr::str_to_lower(c(histology_temp)),
.data$index_ca_seq %in% c({{ index_ca_seq }})
)
} else {
cohort_ca_dx <- pluck(data_synapse, "ca_dx_index") %>%
# re-number index cancer diagnoses
dplyr::group_by(.data$cohort, .data$record_id) %>%
dplyr::mutate(index_ca_seq = 1:n()) %>%
dplyr::ungroup() %>%
# # apply filter(s)
dplyr::filter(
stringr::str_to_lower(.data$institution) %in%
stringr::str_to_lower(c(institution_temp)),
stringr::str_to_lower(.data$stage_dx) %in%
stringr::str_to_lower(c(stage_dx_temp)),
stringr::str_to_lower(.data$ca_hist_brca) %in%
stringr::str_to_lower(c(histology_temp)),
.data$index_ca_seq %in% c({{ index_ca_seq }})
)
}
# pull drug regimens to those patients
# option 1: all drug regimens to all patients in cohort
# regimen_drugs is not specified, regimen_order is not specified
cohort_ca_drugs <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id", "ca_seq"),
pluck(data_synapse, "ca_drugs"),
by = c("cohort", "record_id", "ca_seq")
) %>%
# create order for drug regimen within cancer and within times the
# drug was received
dplyr::group_by(.data$cohort, .data$record_id, .data$ca_seq) %>%
dplyr::arrange(
.data$cohort, .data$record_id,
.data$ca_seq, .data$regimen_number
) %>%
dplyr::mutate(order_within_cancer = 1:n()) %>%
dplyr::ungroup() %>%
# order drugs w/in regimen, have to account for structure of data which is
# 1 reg:assoc ca dx
# (may have more than one row for a drug regimen even if it's the first time
# that drug regimen was received)
dplyr::left_join(.,
pluck(data_synapse, "ca_drugs") %>%
dplyr::distinct(
.data$record_id, .data$regimen_number,
.data$regimen_drugs
) %>%
dplyr::group_by(.data$record_id, .data$regimen_drugs) %>%
dplyr::arrange(
.data$record_id, .data$regimen_number,
.data$regimen_drugs
) %>%
dplyr::mutate(order_within_regimen = 1:n()) %>%
dplyr::ungroup() %>%
dplyr::select(-"regimen_drugs"),
by = c("record_id", "regimen_number")
) %>%
dplyr::left_join(.,
genieBPC::regimen_abbreviations,
by = c("regimen_drugs")
)
# option 2: all "first line" drug regimens (regimens of a certain number,
# within a cancer diagnosis)
# specific regimen number to all pts in cohort, any regimen name
# regimen_drugs is not specified, regimen_order is specified and
# regimen_type = "within cancer"
if (missing(regimen_drugs) && !missing(regimen_order) &&
stringr::str_to_lower(regimen_order_type) == "within cancer") {
# cohort_ca_drugs <- dplyr::left_join(cohort_ca_dx,
# pluck(data_synapse, paste0("ca_drugs_", cohort_temp)),
# by = c("cohort", "record_id", "institution", "ca_seq")
# ) %>%
# dplyr::filter(.data$order_within_cancer %in% c({{ regimen_order }}))
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::filter(.data$order_within_cancer %in% c({{ regimen_order }}))
# restrict cancer cohort to all patients who got a drug regimen
cohort_ca_dx <- dplyr::inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
dplyr::filter(.data$order_within_cancer %in% c({{ regimen_order }})) %>%
dplyr::select("cohort", "record_id", "institution", "ca_seq"),
by = c(
"cohort", "record_id", "institution", "ca_seq"
)
)
}
# if specific drug regimen is requested; exact regimen
# option 3a: all times that exact drug regimen was received
if (!missing(regimen_drugs) && missing(regimen_order) &&
stringr::str_to_lower(regimen_type) == "exact") {
# identify instances of that drug regimen
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::filter(
str_to_lower(.data$regimen_drugs) %in% c(regimen_drugs_sorted) |
str_to_lower(.data$abbreviation) %in% c(regimen_drugs_sorted) #|
# drug_class %in% c(regimen_drugs_sorted)
)
# restrict cancer cohort to patients on that drug regimen
cohort_ca_dx <- dplyr::inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
dplyr::distinct(
.data$cohort, .data$record_id, .data$institution,
.data$ca_seq
),
by = c("cohort", "record_id", "institution", "ca_seq")
)
}
# option 3b: all times that regimen containing drugs was received
if (!missing(regimen_drugs) && missing(regimen_order) &&
stringr::str_to_lower(regimen_type) == "containing") {
# identify instances of that drug regimen
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::filter(grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$regimen_drugs)
) |
grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$abbreviation)
))
# restrict cancer cohort to patients on that drug regimen
cohort_ca_dx <- dplyr::inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
dplyr::distinct(
.data$cohort, .data$record_id, .data$institution,
.data$ca_seq
),
by = c("cohort", "record_id", "institution", "ca_seq")
)
}
# option 4a: 1st (or other) time that exact regimen was received
if (!missing(regimen_drugs) && !missing(regimen_order) &&
stringr::str_to_lower(regimen_order_type) == "within regimen" &&
stringr::str_to_lower(regimen_type) == "exact") {
# identify instances of that drug regimen
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::filter(str_to_lower(.data$regimen_drugs)
%in% c(regimen_drugs_sorted) |
str_to_lower(.data$abbreviation) %in% c(regimen_drugs_sorted)) %>%
# filter on order of interest (e.g. first, all)
dplyr::filter(.data$order_within_regimen %in% c({{ regimen_order }}))
# restrict cancer cohort to patients on that drug regimen
cohort_ca_dx <- dplyr::inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
distinct(
.data$cohort, .data$record_id, .data$institution,
.data$ca_seq
),
by = c("cohort", "record_id", "institution", "ca_seq")
)
}
# option 4b: 1st (or other) time that regimen containing was received
if (!missing(regimen_drugs) &&
!missing(regimen_order) &&
stringr::str_to_lower(regimen_order_type) == "within regimen" &&
stringr::str_to_lower(regimen_type) == "containing") {
# identify instances of that drug regimen
# have to start with full drugs dataset for 'within regimen',
# otherwise are left with all drug regimens to pts in this cohort
cohort_ca_drugs <- pluck(data_synapse, "ca_drugs") %>%
# add on abbreviations
dplyr::left_join(.,
genieBPC::regimen_abbreviations,
by = c("regimen_drugs")
) %>%
# create new order b/c this is regimen CONTAINING drugs listed
# order drugs w/in regimen, have to account for
# structure of data which is
# 1 reg:assoc ca dx
# (may have more than one row for a drug regimen even
# if it's the first time
# that drug regimen was received)
# have to filter on containing regimens first, then re-number
dplyr::filter(grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$regimen_drugs)
) |
grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$abbreviation)
)) %>%
# now re-number w/in containing regimens
dplyr::left_join(.,
pluck(data_synapse, "ca_drugs") %>%
# add on abbreviations
dplyr::left_join(.,
genieBPC::regimen_abbreviations,
by = c("regimen_drugs")
) %>%
# get regimens containing drugs of interest
dplyr::filter(grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$regimen_drugs)
) |
grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$abbreviation)
)) %>%
# get distinct regimen administrations (since regs
# potentially mapped to multiple ca types)
dplyr::distinct(
.data$record_id, .data$regimen_number,
.data$regimen_drugs
) %>%
# order regimens
dplyr::group_by(.data$record_id) %>%
dplyr::arrange(
.data$record_id, .data$regimen_number,
.data$regimen_drugs
) %>%
dplyr::mutate(
order_within_containing_regimen = 1:n()
) %>%
dplyr::ungroup() %>%
dplyr::select(-"regimen_drugs"),
by = c("record_id", "regimen_number")
) %>%
# filter on order of interest (e.g. first, all)
dplyr::filter(.data$order_within_containing_regimen
%in% c({{ regimen_order }})) %>%
# restrict to patients in the cohort (started with all regimens to all
# patients)
dplyr::inner_join(.,
cohort_ca_dx %>%
dplyr::select("cohort", "record_id", "ca_seq"),
by = c("cohort", "record_id", "ca_seq")
) %>%
# create blank variables (dropped below, not having them is unique to
# regimen_order_type = 'containing')
mutate(
order_within_cancer = as.numeric(NA),
order_within_regimen = as.numeric(NA)
)
# restrict cancer cohort to patients on that drug regimen
cohort_ca_dx <- inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
dplyr::distinct(
.data$cohort, .data$record_id, .data$institution,
.data$ca_seq
),
by = c("cohort", "record_id", "institution", "ca_seq")
)
}
# option 5a: specific drugs within a cancer diagnosis, exact regimen
if (!missing(regimen_drugs) &&
!missing(regimen_order) &&
stringr::str_to_lower(regimen_type) == "exact" &&
stringr::str_to_lower(regimen_order_type) == "within cancer") {
# identify instances of that drug regimen
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::filter(
str_to_lower(.data$regimen_drugs) %in% c(regimen_drugs_sorted) |
str_to_lower(.data$abbreviation) %in% c(regimen_drugs_sorted),
.data$order_within_cancer %in% c({{ regimen_order }})
)
# restrict cancer cohort to patients on that drug regimen
cohort_ca_dx <- dplyr::inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
distinct(
.data$cohort, .data$record_id, .data$institution,
.data$ca_seq
),
by = c("cohort", "record_id", "institution", "ca_seq")
)
}
# option 5b: specific drugs within a cancer diagnosis, regimen containing
if (!missing(regimen_drugs) &&
!missing(regimen_order) &&
stringr::str_to_lower(regimen_type) == "containing" &&
stringr::str_to_lower(regimen_order_type) == "within cancer") {
# identify instances of that drug regimen
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::filter(
grepl(paste(regimen_drugs_sorted,
collapse = "|"
), str_to_lower(.data$regimen_drugs)) |
grepl(
paste(regimen_drugs_sorted, collapse = "|"),
str_to_lower(.data$abbreviation)
),
.data$order_within_cancer %in% c({{ regimen_order }})
)
# restrict cancer cohort to patients on that drug regimen
cohort_ca_dx <- dplyr::inner_join(cohort_ca_dx,
cohort_ca_drugs %>%
dplyr::distinct(
.data$cohort, .data$record_id, .data$institution,
.data$ca_seq
),
by = c("cohort", "record_id", "institution", "ca_seq")
)
}
# for patients meeting the specified criteria, also pull related datasets
# patient characteristics
cohort_pt_char <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id"),
pluck(data_synapse, "pt_char"),
by = c("cohort", "record_id")
)
# non-index cancer
cohort_ca_dx_non_index <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id"),
pluck(data_synapse, "ca_dx_non_index"),
by = c("cohort", "record_id")
)
# PRISSMM Path
cohort_prissmm_pathology <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id"),
pluck(data_synapse, "prissmm_pathology"),
by = c("cohort", "record_id")
)
# PRISSMM Imaging
cohort_prissmm_imaging <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id"),
pluck(data_synapse, "prissmm_imaging"),
by = c("cohort", "record_id")
)
# PRISSMM Med Onc
cohort_prissmm_md <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id"),
pluck(data_synapse, "prissmm_md"),
by = c("cohort", "record_id")
)
# TM (if applicable)
if (!is.null(pluck(data_synapse, "tumor_marker"))) {
cohort_tumor_marker <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id"),
pluck(data_synapse, "tumor_marker"),
by = c("cohort", "record_id")
)
}
# RT (if applicable)
if (!is.null(pluck(data_synapse, "ca_radtx"))) {
cohort_ca_radtx <- dplyr::inner_join(cohort_ca_dx %>%
dplyr::select("cohort", "record_id", "ca_seq"),
pluck(data_synapse, "ca_radtx"),
by = c("cohort", "record_id", "ca_seq")
)
}
# cancer panel test information
# keep records based on record_id + cancer sequence of interest
cohort_ngs <- dplyr::inner_join(
cohort_ca_dx %>%
dplyr::select("cohort", "record_id", "ca_seq"),
pluck(data_synapse, "cpt"),
by = c("cohort", "record_id", "ca_seq")
) %>%
distinct()
if(any(names(cohort_ngs) == "cpt_sample_type") &
!any(names(cohort_ngs) == "sample_type")){
cohort_ngs <- cohort_ngs %>%
dplyr::mutate(sample_type = case_when(
str_to_lower(.data$cpt_sample_type)
%in% c("1", "primary", "primary tumor") ~ "Primary tumor",
str_to_lower(.data$cpt_sample_type)
%in% c("2", "lymph node metastasis") ~ "Lymph node metastasis",
str_to_lower(.data$cpt_sample_type)
%in% c("3", "distant organ metastasis") ~ "Distant organ metastasis",
str_to_lower(.data$cpt_sample_type)
%in% c("4", "metastasis site unspecified", "metastatic recurrence") ~
"Metastasis site unspecified",
str_to_lower(.data$cpt_sample_type)
%in% c("5", "local recurrence") ~ "Local recurrence",
str_to_lower(.data$cpt_sample_type)
%in% c("6", "unspecified") ~ as.character(NA),
str_to_lower(.data$cpt_sample_type)
%in% c("7", "not applicable or hematologic malignancy") ~
"Not applicable or hematologic malignancy"
))
}
# genomic sequencing information
if (!is.null(pluck(data_synapse, "fusions"))) {
cohort_fusions <- dplyr::inner_join(pluck(data_synapse, "fusions"),
cohort_ngs %>%
dplyr::select("cohort", "cpt_genie_sample_id"),
by = c("Tumor_Sample_Barcode" = "cpt_genie_sample_id")
)
}
if (!is.null(pluck(data_synapse, "mutations_extended"))) {
cohort_mutations_extended <- dplyr::inner_join(pluck(data_synapse,
"mutations_extended"),
cohort_ngs %>%
dplyr::select("cohort", "cpt_genie_sample_id"),
by = c("Tumor_Sample_Barcode" = "cpt_genie_sample_id")
)
}
# cna file is 1 col / tumor sample barcode
if (!is.null(pluck(data_synapse, "cna"))) {
# get list of IDs to keep
cpt_barcode_keep <- pluck(data_synapse, "cpt") %>%
mutate(
Tumor_Sample_Barcode =
stringr::str_replace_all(.data$cpt_genie_sample_id,
pattern = "-",
replacement = "\\."
)
) %>%
pull("Tumor_Sample_Barcode")
cohort_cna <- pluck(data_synapse, "cna") %>%
select("Hugo_Symbol", any_of(cpt_barcode_keep))
}
# if 0 patients are returned
if (nrow(cohort_ca_dx) == 0) {
message("No patients meeting the specified criteria were returned.
Ensure that all parameters were correctly specified. Specifically,
the list of acceptable drugs can be found in the
`drug_regimen_list` dataset available with this package.")
}
# return a table 1 to describe the cancer cohort if the user specifies
if (nrow(cohort_ca_dx) > 0 && return_summary == TRUE) {
# number of records per patient in the diagnosis dataset
n_rec_dx_dset <- cohort_ca_dx %>%
dplyr::group_by(.data$record_id) %>%
dplyr::summarize(n_rec_pt = n(), .groups = "drop") %>%
gtsummary::tbl_summary(
include = "n_rec_pt",
label = n_rec_pt ~ "Number of diagnoses per patient in cohort_ca_dx
data frame",
type = n_rec_pt ~ "categorical"
) %>%
gtsummary::modify_header(
update = list(
stat_0 ~ "**N = {N} patients**"
),
quiet = TRUE
)
n_rec_drugs_dset <- cohort_ca_drugs %>%
dplyr::group_by(.data$record_id) %>%
dplyr::summarize(n_rec_pt = n(), .groups = "drop") %>%
gtsummary::tbl_summary(
include = "n_rec_pt",
label = n_rec_pt ~ "Number of regimens per patient in cohort_ca_drugs
data frame",
type = n_rec_pt ~ "categorical"
)
n_rec_cpt_dset <- cohort_ngs %>%
dplyr::group_by(.data$record_id) %>%
dplyr::summarize(n_rec_pt = n(), .groups = "drop") %>%
gtsummary::tbl_summary(
include = "n_rec_pt",
label = n_rec_pt ~ "Number of CPTs per patient in cohort_ngs
data frame",
type = n_rec_pt ~ "categorical"
)
tbl_overall_summary <- gtsummary::tbl_stack(
tbls = list(
n_rec_dx_dset,
n_rec_drugs_dset,
n_rec_cpt_dset
),
quiet = TRUE
) %>%
gtsummary::bold_labels()
if (cohort_temp != "BrCa") {
tbl_cohort <- cohort_ca_dx %>%
gtsummary::tbl_summary(
include = c(
"cohort", "institution",
"stage_dx", "ca_hist_adeno_squamous"
),
label = list(
cohort ~ "Cohort (cohort)",
institution ~ "Institution (institution)",
stage_dx ~ "Stage at diagnosis (stage_dx)",
ca_hist_adeno_squamous ~ "Histology (ca_hist_adeno_squamous)"
)
) %>%
gtsummary::bold_labels() %>%
gtsummary::modify_header(
update = list(
stat_0 ~ "**N = {N} Diagnoses**"
),
quiet = TRUE
)
} else {
tbl_cohort <- cohort_ca_dx %>%
# dplyr::group_by(.data$record_id) %>%
# dplyr::mutate(n_rec_pt = n()) %>%
# dplyr::ungroup() %>%
gtsummary::tbl_summary(
include = c("cohort", "institution", "stage_dx",
"ca_hist_brca"),
label = list(
cohort ~ "Cohort (cohort)",
institution ~ "Institution (institution)",
stage_dx ~ "Stage at diagnosis (stage_dx)",
ca_hist_brca ~ "Histology (ca_hist_brca)"
)
) %>%
gtsummary::bold_labels() %>%
gtsummary::modify_header(
update = list(
stat_0 ~ "**N = {N} Diagnoses**"
),
quiet = TRUE
)
}
tbl_drugs <- cohort_ca_drugs %>%
gtsummary::tbl_summary(
include = c("cohort", "institution", "regimen_drugs"),
label = list(
cohort ~ "Cohort (cohort)",
institution ~ "Institution (institution)",
regimen_drugs ~ "Drugs in regimen (regimen_drugs)"
)
) %>%
gtsummary::bold_labels() %>%
gtsummary::modify_header(
update = list(
stat_0 ~ "**N = {N} Regimens**"
),
quiet = TRUE
)
tbl_ngs <- cohort_ngs %>%
gtsummary::tbl_summary(
include = c("cohort", "institution",
"cpt_oncotree_code", "cpt_seq_assay_id"),
label = list(
cohort ~ "Cohort (cohort)",
institution ~ "Institution (institution)",
cpt_oncotree_code ~ "OncoTree code (cpt_oncotree_code)",
cpt_seq_assay_id ~ "Sequence assay ID (cpt_seq_assay_id)"
)
) %>%
gtsummary::bold_labels() %>%
gtsummary::modify_header(
update = list(
stat_0 ~ "**N = {N} Cancer Panel Tests**"
),
quiet = TRUE
)
}
# drop variable before returning data frame
cohort_ca_dx <- cohort_ca_dx %>% select(-"index_ca_seq")
cohort_ca_drugs <- cohort_ca_drugs %>%
dplyr::select(-"order_within_cancer",
-"order_within_regimen",
-"abbreviation")
# order of dataframes, should they exist
df_order <- c(
"cohort_pt_char", "cohort_ca_dx",
"cohort_ca_dx_non_index",
"cohort_ca_radtx", "cohort_ca_drugs",
"cohort_prissmm_imaging", "cohort_prissmm_pathology",
"cohort_prissmm_md", "cohort_tumor_marker",
"cohort_ngs",
"cohort_mutations_extended", "cohort_fusions", "cohort_cna",
"tbl_overall_summary", "tbl_cohort", "tbl_drugs", "tbl_ngs"
)
# return data frames & tables that are present in the function's environment
rtn <- mget(ls(environment(), pattern = "^cohort_|^tbl"),
envir = environment()
)
# save elements on list in order that we want (clinical datasets, genomic
# datasets, tables) and drop any items that don't appear in this run of
# create_analytic_cohort
rtn_ordered <- rtn[c(df_order)] %>%
purrr::compact()
if (nrow(cohort_ca_dx) > 0) {
return(rtn_ordered)
}
} # end of function
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