ctrdataRemember to respect the registers’ terms and conditions (see
ctrOpenSearchPagesInBrowser(copyright = TRUE)). Please cite
this package in any publication as follows: Ralf Herold (2025). ctrdata:
Retrieve and Analyze Clinical Trials in Public Registers. R package
version 1.21.0. https://cran.r-project.org/package=ctrdata
These functions open the browser, where the user can start searching for trials of interest.
Refine the search until the trials of interest are listed in the
browser. The total number of trials that can be retrieved with package
ctrdata is intentionally limited to queries with at most
10,000 result records.
Use functions or keyboard shortcuts according to the operating system.
See here for our automation to copy the URLs of a user’s queries in any of the supported clinical trial registers.
The next steps are executed in the R environment:
q <- ctrGetQueryUrl()
# * Using clipboard content as register query URL: https://www.clinicaltrialsregister.eu/
# ctr-search/search?query=cancer&age=under-18&resultsstatus=trials-with-results
# * Found search query from EUCTR: query=cancer&age=under-18&resultsstatus=trials-with-results
q
# query-term query-register
# 1 query=cancer&age=under-18&resultsstatus=trials-with-results EUCTR
# To check, this opens a browser with the query
ctrOpenSearchPagesInBrowser(url = q)Note that in addition to protocol- and results-related information,
also all trial documents that made publicly available by the registers,
including any protocols, consent forms and results reports, can be
downloaded by ctrdata, by specifying parameter
documents.path, see
help(ctrLoadQueryIntoDb).
# Count number of trial records
ctrLoadQueryIntoDb(
queryterm = q,
only.count = TRUE
)$n
# * Checking trials in EUCTR...
# Retrieved overview, multiple records of 376 trial(s) from 19 page(s) to be
# downloaded (estimate: 50 MB)
# [1] 376
# Connect to a database and chose a collection (table)
db <- nodbi::src_sqlite(
dbname = "sqlite_file.sql",
collection = "test"
)
# Retrieve records, download into database
ctrLoadQueryIntoDb(
queryterm = q,
con = db
)
# Show which queries have been downloaded into database
dbQueryHistory(con = db)
# query-timestamp query-register query-records
# 1 2025-03-02 13:05:08 EUCTR 1470
# query-term
# 1 query=cancer&age=under-18&resultsstatus=trials-with-resultsWith any database, this takes about 80 seconds for 1470 records of 376 trials (~55 ms/record), with most of the time needed for internet-retrieval which is slow from this register.
Previously executed queries can be repeated by specifying “last” or
an integer number for parameter querytoupdate, where the
number corresponds to the row number of the query shown with
dbQueryHistory(). Where possible, the query to update first
checks for new records in the register. Depending on the register and
time since running the query last, an update (differential update) is
possible or the original query is executed fully again.
The same collection can be used to store (and analyse) trial
information from different registers, thus can include different and
complementary sets of trials. The registers currently supported include
CTIS, EUCTR, CTGOV, CTGOV2 and ISRCTN. This can be achieved by loading
queries that the user defines specifically or that function
ctrGenerateQueries() provides, as follows:
# Loading specific query into same collection
ctrLoadQueryIntoDb(
queryterm = "cond=neuroblastoma&aggFilters=phase:2,ages:child,status:com",
register = "CTGOV2",
con = db
)
# Use same query details to obtain queries
queries <- ctrGenerateQueries(
condition = "neuroblastoma",
recruitment = "completed",
phase = "phase 2",
population = "P"
)
# Open queries in registers' web interfaces
sapply(queries, ctrOpenSearchPagesInBrowser)
# Load all queries into database collection
result <- lapply(queries, ctrLoadQueryIntoDb, con = db)
# Show results of loading
sapply(result, "[[", "n")
# EUCTR CTGOV2 ISRCTN CTIS
# 180 111 0 1
# Overview of queries
dbQueryHistory(con = db)
# query-timestamp query-register query-records
# 1 2025-03-02 13:05:08 EUCTR 1470
# 2 2025-03-02 13:26:49 EUCTR 0
# 3 2025-03-02 13:31:53 EUCTR 1470
# 4 2025-03-02 13:49:09 CTGOV2 111
# 5 2025-03-02 13:56:01 EUCTR 180
# 6 2025-03-02 13:56:02 CTGOV2 111
# 7 2025-03-02 13:56:03 CTIS 1
# query-term
# 1 query=cancer&age=under-18&resultsstatus=trials-with-results
# 2 query=cancer&age=under-18&resultsstatus=trials-with-results
# 3 query=cancer&age=under-18&resultsstatus=trials-with-results
# 4 cond=neuroblastoma&aggFilters=phase:2,ages:child,status:com
# 5 query=neuroblastoma&phase=phase-two&age=children&age=under-18&status=completed
# 6 cond=neuroblastoma&aggFilters=phase:2,ages:child,status:com
# 7 searchCriteria={"medicalCondition":"neuroblastoma","trialPhaseCode":[4],"ageGroupCode":[2],"status":[5,8]}When loading trial information, the user can specify an annotation
string to each of the records that are loaded when calling
ctrLoadQueryIntoDb(). By default, new annotations are
appended to any existing annotation of the trial record; alternatively,
annotations can be replaced. Annotations are useful for analyses, for
example to specially identify subsets of records and trials of interest
in the collection
# Annotate a query in CTGOV2 defined above
ctrLoadQueryIntoDb(
queryterm = queries["CTGOV2"],
annotation.text = "site_DE ",
annotation.mode = "append",
con = db
)
# * Appears specific for CTGOV REST API 2.0
# * Found search query from CTGOV2: cond=neuroblastoma&aggFilters=phase:2,ages:child,status:com
# * Checking trials using CTGOV REST API 2.0, found 111 trials
# (1/3) Downloading in 1 batch(es) (max. 1000 trials each; estimate: 11 Mb total)
# (2/3) Converting to NDJSON...
# (3/3) Importing records into database...
# JSON file #: 1 / 1
# = Imported or updated 111 trial(s)
# = Annotated retrieved records (111 records)
# Updated history ("meta-info" in "test")
# $n
# [1] 111When identifiers of clinical trials of interest are already known,
this example shows how they can be processed to import the trial
information into a database collection. This involves constructing a
query that combines the identifiers and then iterating over the sets of
identifiers. Note to combine identifiers into the queryterm
depends on the specific register:
# ids of trials of interest
ctIds <- c(
"NCT00001209", "NCT00001436", "NCT00187109", "NCT01516567", "NCT01471782",
"NCT00357084", "NCT00357500", "NCT00365755", "NCT00407433", "NCT00410657",
"NCT00436852", "NCT00445965", "NCT00450307", "NCT00450827", "NCT00471679",
"NCT00492167", "NCT00499616", "NCT00503724"
)
# split into sets of each 10 trial ids
# (larger sets e.g. 50 may still work)
idSets <- split(ctIds, ceiling(seq_along(ctIds) / 10))
# variable to collect import results
result <- NULL
# iterate over sets of trial ids
for (idSet in idSets) {
setResult <- ctrLoadQueryIntoDb(
queryterm = paste0("term=", paste0(idSet, collapse = " ")),
register = "CTGOV2",
con = db
)
# check that queried ids have
# successfully been loaded
stopifnot(identical(
sort(setResult$success), sort(idSet)))
# append result
result <- c(result, list(setResult))
}
# inspect results
as.data.frame(do.call(rbind, result))[, c("n", "failed")]
# n failed
# 1 10 NULL
# 2 8 NULL
# queryterms for other registers for retrieving trials by their identifier:
#
# CTIS (note the comma separated values):
# https://euclinicaltrials.eu/ctis-public/search#searchCriteria=
# {"containAny":"2025-521008-22-00, 2024-519446-67-00, 2024-517647-31-00"}
#
# EUCTR (note the country suffix os to be removed, values separated with OR):
# https://www.clinicaltrialsregister.eu/ctr-search/search?
# query=2008-001606-16+OR+2008-001721-34+OR+2008-002260-33
#Not all registers automatically expand search terms to include alternative terms, such as codes and other names of active substances. The synonymous names can be used in queries in a register that does not offer search expansion. To obtain a character vector of synonyms for an active substance name:
The interest is increasing to design and use integrated research
platforms, clinical research platforms, platform trials, multi-arm
multi-stage (MAMS) and master protocol research programs (MPRPs).
Additional concepts and terms used include basket and umbrella trials,
and in particular complex trials. Please see the references below for
further information. ctrdata can help finding such research
and analysing the study information, as follows:
# Generate queries to identify trials
urls <- ctrGenerateQueries(
searchPhrase = paste0(
"basket OR platform OR umbrella OR master protocol OR ",
"multiarm OR multistage OR subprotocol OR substudy OR ",
"multi-arm OR multi-stage OR sub-protocol OR sub-study"
))
# See
help("ctrGenerateQueries")
# Open queries in register web interface
sapply(urls, ctrOpenSearchPagesInBrowser)
# Count number of studies found in the register
result <- lapply(urls, ctrLoadQueryIntoDb, only.count = TRUE)
sapply(result, "[[", "n")
# EUCTR CTGOV2 ISRCTN CTIS
# 2808 2372 1426 289
# For analysis, consider using trial concepts, see
help("ctrdata-trial-concepts")
# and see examples in
vignette("ctrdata_summarise")References:
This example works with a free service here. Note that the user name and password need to be encoded. The format of the connection string is documented at https://www.mongodb.com/docs/manual/reference/connection-string/. For recommended databases, see vignette Install R package ctrdata.
# Specify base uri for remote MongoDB server,
# as part of the encoded connection string
db <- nodbi::src_mongo(
# Note: this provides read-only access
url = "mongodb+srv://DWbJ7Wh:bdTHh5cS@cluster0-b9wpw.mongodb.net",
db = "dbperm",
collection = "dbperm")
# Since the above access is read-only,
# just obtain fields of interest:
dbGetFieldsIntoDf(
fields = c("a2_eudract_number",
"e71_human_pharmacology_phase_i"),
con = db)
# _id a2_eudract_number e71_human_pharmacology_phase_i
# 1 2010-024264-18-3RD 2010-024264-18 TRUE
# 2 2010-024264-18-AT 2010-024264-18 TRUE
# 3 2010-024264-18-DE 2010-024264-18 TRUE
# 4 2010-024264-18-GB 2010-024264-18 TRUE
# 5 2010-024264-18-IT 2010-024264-18 TRUE
# 6 2010-024264-18-NL 2010-024264-18 TRUE