Retrieve clinical trial information

Get started

Attach package `ctrdata`

library(ctrdata)
citation("ctrdata")

Remember 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 1.22.3. https://cran.r-project.org/package=ctrdata

Open register’s advanced search page in browser

These functions open the browser, where the user can start searching for trials of interest.

# Please review and respect register copyrights:
ctrOpenSearchPagesInBrowser(
  copyright = TRUE
)

# Open browser with example search:
ctrOpenSearchPagesInBrowser(
  url = "cancer&age=under-18&resultsstatus=trials-with-results",
  register = "EUCTR"
)

Adjust search parameters and execute search in browser

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.

Copy address from browser address bar to clipboard

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.

Get address from clipboard

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)

Connect database, retrieve, transform, save and check

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
# * Found search query from EUCTR: query=cancer&age=under-18&resultsstatus=trials-with-results
# * Checking trials in EUCTR, found 390 trials 
# [1] 390

# Connect to a database and chose a collection (table)
db <- nodbi::src_sqlite(
  dbname = "database_name.sql",
  collection = "test"
)

# Retrieve records, load into database
ctrLoadQueryIntoDb(
  queryterm = q,
  con = db
)
# * Found search query from EUCTR: query=cancer&age=under-18&resultsstatus=trials-with-results
# * Checking trials in EUCTR, found 390 trials 
# - Downloading in 20 batch(es) (20 trials each; estimate: 50 MB)
# - Downloading 1541 records of 390 trials (estimate: 90 s)            
# - Converting to NDJSON (estimate: 3 s) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
# - Importing records into database...
# = Imported or updated 1541 records on 390 trial(s) 
# No history found in expected format.
# Updated history ("meta-info" in "test")
# $n
# [1] 1541

# Show which queries have been downloaded into database
dbQueryHistory(con = db)
#       query-timestamp query-register query-records
# 1 2025-07-02 20:27:25          EUCTR          1541
#                                                    query-term
# 1 query=cancer&age=under-18&resultsstatus=trials-with-results

With any database, this takes about 90 seconds for 1541 records of 390 trials (~60 ms/record).

Repeat and update a previous query

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.

# Show all queries
dbQueryHistory(con = db)

# Repeat last query
ctrLoadQueryIntoDb(
  querytoupdate = "last",
  only.count = TRUE,
  con = db
)
# Found search query from EUCTR: query=cancer&age=under-18&resultsstatus=trials-with-results
# * Query last run: 2025-07-02 20:27:25
# * Checking for new or updated trials...
# First result page empty - no (new) trials found?
# Updated history ("meta-info" in "test")
# $n
# [1] 0

Results-related trial information

For CTGOV and CTGOV2, any results are always included in the retrieval. Only for EUCTR, result-related trial information has to be requested to be retrieved, because it will take longer to download and store. No results in structured electronic format are foreseeably available from ISRCTN and CTIS, thus ctrdata cannot load them, see help(ctrLoadQueryIntoDb). The download or presence of results is not recorded in dbQueryHistory() because the availability of results increases over time. The following takes in total only 120 seconds for 1541 records (~ 80 ms/record) of 390 trials, if run in the same R session as the above commands and thus re-using previous downloads of trials.

ctrLoadQueryIntoDb(
  querytoupdate = "last",
  forcetoupdate = TRUE,
  euctrresults = TRUE,
  con = db
)
# * Found search query from EUCTR: query=cancer&age=under-18&resultsstatus=trials-with-results
# * Query last run: 2025-07-02 20:27:25
# * Checking trials in EUCTR, found 390 trials 
# - Downloading in 20 batch(es) (20 trials each; estimate: 50 MB)
# - Downloading 1541 records of 390 trials (estimate: 90 s)
# - Converting to NDJSON (estimate: 3 s) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
# - Importing records into database...
# = Imported or updated 1541 records on 390 trial(s) 
# * Checking results if available from EUCTR for 390 trials: 
# - Downloading results...
# - Extracting results (. = data, F = file[s] and data, x = none): F F . . . . 
# F . . F . . . F . . F F F . . . . F F F . . F . . . . F F . F . . . . . . . . 
# F . . . . . . . . . . . F . F . . . . . . . F . . . . . . . . . . . . . . . F 
# . . . . . . F . . F F F . . . . F F . . F . . . . . . . . . . . F . . . . . . 
# . F . . . . . . . . . . . F . . . . . . F . . . . . . . . . F . F . . . . . .
# . . . . . F . . . F F . . F . . . . . . . . . . . . F . . . . . F . . . . . . 
# F . . . . . F . . . F F . . . . . . F . F . . . . . F . . F . . . . F . . . . 
# . . . . . . . . . F F . F . . . F . . . F F . . . . . . . . . . . F . . . . F 
# . . . . . . F F . F . . . . . . . . . . F F . F F . . . . F F . . . F . F . . 
# . F . . . . . . . . . . . F . F . . . . . F . F . . . F . F . . F . F . . F . 
# . . . F F F . . . . . . . . . . . . F . . . . . . . . . . . . . . 
# - Converting to NDJSON (estimate: 40 s)...
# - Importing results into database (may take some time)...
# - Results history: not retrieved (euctrresultshistory = FALSE)
# = Imported or updated results for 390 trials
# Updated history ("meta-info" in "test")
# $n
# [1] 1541

Add trial information from several registers

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
)
# * Found search query from CTGOV2: cond=neuroblastoma&aggFilters=phase:2,ages:child,status:com
# * Checking trials in CTGOV, found 110 trials
# - Downloading in 1 batch(es) (max. 1000 trials each; estimate: 11 Mb total)
# - Converting to NDJSON...
# - Importing records into database...
# JSON file #: 1 / 1                               
# = Imported or updated 110 trial(s)
# Updated history ("meta-info" in "test")
# $n
# [1] 110

# 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       ISRCTN       CTGOV2 CTGOV2expert         CTIS 
#   180            0          110          110            1 

# Overview of queries
dbQueryHistory(con = db)
#       query-timestamp query-register query-records
# 1 2025-07-02 20:27:25          EUCTR          1541
# 2 2025-07-02 20:30:46          EUCTR             0
# 3 2025-07-02 20:33:38          EUCTR          1541
# 4 2025-07-02 20:39:41         CTGOV2           110
# 5 2025-07-02 20:40:18          EUCTR           180
# 6 2025-07-02 20:40:20         CTGOV2           110
# 7 2025-07-02 20:40:21         CTGOV2           110
# 8 2025-07-02 20:40:22           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=adolescent&age=infant-and-toddler&age=newborn&age=preterm-new-born-infants&age=under-18&status=completed
# 6 cond=neuroblastoma&aggFilters=phase:2,ages:child,status:com,studyType:int
# 7 term=AREA[ConditionSearch]"neuroblastoma" AND (AREA[Phase]"PHASE2") AND (AREA[StdAge]"CHILD") AND (AREA[OverallStatus]"COMPLETED") AND (AREA[StudyType]INTERVENTIONAL)
# 8 searchCriteria={"medicalCondition":"neuroblastoma","trialPhaseCode":[4],"ageGroupCode":[2],"status":[5,8]}

Add personal annotations

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
)
# * Found search query from CTGOV2: cond=neuroblastoma&aggFilters=phase:2,ages:child,status:com,studyType:int
# * Checking trials in CTGOV, found 110 trials
# - Downloading in 1 batch(es) (max. 1000 trials each; estimate: 11 Mb total)
# - Converting to NDJSON...
# - Importing records into database...
# JSON file #: 1 / 1                               
# = Imported or updated 110 trial(s)
# = Annotated retrieved records (110 records)
# Updated history ("meta-info" in "test")
# $n
# [1] 110

Find synonyms of active substance names

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:

# Search for synonyms
ctrFindActiveSubstanceSynonyms(
  activesubstance = "imatinib"
)
#  [1] "imatinib"          "CGP 57148"         "CGP 57148B"
#  [4] "CGP57148B"         "Gleevec"           "GLIVEC"
#  [7] "Imatinib"          "Imatinib Mesylate" "NSC 716051"
# [10] "ST1571"            "STI 571"           "STI571"

Find platform trials

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
queries <- 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"), 
  startAfter = "2015-01-01")

# See 
help("ctrGenerateQueries")

# Open queries in register web interface
sapply(queries, ctrOpenSearchPagesInBrowser)

# Count number of studies found in the register
result <- lapply(queries, ctrLoadQueryIntoDb, only.count = TRUE)

sapply(result, "[[", "n")
# EUCTR       ISRCTN       CTGOV2 CTGOV2expert         CTIS 
#  1635          165         1559         1559          237 

# Note that for EUCTR, the number of trials is shown; 
# since each trial can have multiple records in this
# register, the number is higher when actually loaded

# Connect to a database and chose a collection (table)
db <- nodbi::src_sqlite(
  dbname = "database_name.sql",
  collection = "test"
)

# Load studies, include EUCTR results data for analysis
result <- lapply(queries, ctrLoadQueryIntoDb, con = db, euctrresults = TRUE)

sapply(result, "[[", "n")
# EUCTR       ISRCTN       CTGOV2 CTGOV2expert         CTIS 
#  7673          166         1562         1562          243 

# See next section for adding related trials

References:

Load information using trial identifiers

When 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.

# Use a trial concept to calculate related identifiers
help("ctrdata-trial-concepts")

# Get data from trials loaded above
df <- dbGetFieldsIntoDf(
  fields = "ctrname",
  calculate = c(
    "f.isUniqueTrial",
    "f.likelyPlatformTrial",
    "f.trialTitle"
  ),
  con = db
)
# To review trial concepts details, call 'help("ctrdata-trial-concepts")'
# Querying database (25 fields)...
# Searching for duplicate trials...                         
# - Getting all trial identifiers (may take some time), 9644 found in collection
# - Finding duplicates among registers' and sponsor ids...
# - 5596 EUCTR _id were not preferred EU Member State record for 1767 trials
# - Keeping 1562 / 1479 / 0 / 124 / 98 records from CTGOV2 / EUCTR / CTGOV / ISRCTN / CTIS
# = Returning keys (_id) of 3263 records in collection "test"
# Searching for duplicate trials... ..                            
# - Getting all trial identifiers, 9644 found in collection
# Calculating f.trialTitle...    

# Show names of calculated columns in the
# data frame with possible platform trials
names(df)
# [1] "_id"                 
# [2] "ctrname"             
# [3] ".isUniqueTrial"      
# [4] ".likelyPlatformTrial"
# [5] ".likelyRelatedTrials"
# [6] ".maybeRelatedTrials" 
# [7] ".trialTitle" 

# Reduce to unique trials
df <- df[df$.isUniqueTrial, ]
nrow(df)
# [1] 3263

# Number of recognised set of trials
length(unique(df$.maybeRelatedTrials))
# 181

# Trials with which _id are missing? 
missingIds <- na.omit(setdiff(unlist(df$.maybeRelatedTrials), df$`_id`))

# Load missing trials by _id
res <- list()
for (i in seq_along(missingIds)) {
  message(i, ": ", missingIds[i])
  res <- c(res, suppressMessages(
    list(ctrLoadQueryIntoDb(missingIds[i], euctrresults = TRUE, con = db))))
}

# Trials that could not be loaded are likely phase 1 trials 
# which are not publicly accessible in the in EUCTR register
missingIds[which(sapply(res, "[[", "n") == 0L)]

The above loads one trial after the other, just using the _id of the trial, from which ctrdata infers the concerned register. Alternatively, batches of _ids can be loaded from some registers (not CTIS), as follows.

# 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

Example using MongoDB

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