Main features • Installation • Overview • Databases • Example workflow • Analysis across trials • Tests • Acknowledgements • Future

ctrdata for aggregating and analysing clinical trials

The package ctrdata provides functions for retrieving (downloading) information on clinical trials from public registers, and for aggregating and analysing this information; it can be used for the

• EU Clinical Trials Register (“EUCTR”, https://www.clinicaltrialsregister.eu/)
• ClinicalTrials.gov (“CTGOV” or 🔔“CTGOV2”, see example)
• ISRCTN (https://www.isrctn.com/)
• EU Clinical Trials Information System (“CTIS”, https://euclinicaltrials.eu/) 🔔 see example

The motivation is to investigate and understand trends in design and conduct of trials, their availability for patients and to facilitate using their detailed results for research and meta-analyses. ctrdata is a package for the R system, but other systems and tools can be used with the databases created with the package. This README was reviewed on 2023-11-24 for version 1.16.0 (major improvement: remove all dependencies on external tools).

Main features

• Protocol- and results-related trial information is easily downloaded: Users define a query in a register’s web interface, then copy the URL and enter it into ctrdata which retrieves in one go all trials found. A script can automate copying the query URL from all registers. Documents in registers on trials can also be downloaded. Personal annotations can be made when downloading trials.
• Downloaded trial information is transformed and stored in a collection of a document-centric database, for fast and offline access. Information from different registers can be accumalated in a single collection. Uses DuckDB, PostgreSQL, RSQLite or MongoDB, via R package nodbi: see section Databases below. Re-run any previous query in a collection to retrieve and update trial records.
• For analyses, convenience functions in ctrdata allow find synonyms of an active substance, to identify unique (de-duplicated) trial records across all registers, to merge and recode fields as well as to easily access deeply-nested fields. Analysis can be done with R or other systems, using the JSON-structured information in the database.

Remember to respect the registers’ terms and conditions (see ctrOpenSearchPagesInBrowser(copyright = TRUE)). Please cite this package in any publication as follows: “Ralf Herold (2023). ctrdata: Retrieve and Analyze Clinical Trials in Public Registers. R package version 1.16.0, https://cran.r-project.org/package=ctrdata”.

References

Package ctrdata has been used for:

• Lasch et al. (2022) The Impact of COVID‐19 on the Initiation of Clinical Trials in Europe and the United States. Clinical Pharmacology & Therapeutics, https://doi.org/10.1002/cpt.2534
• Blogging on Innovation coming to paediatric research
• Cancer Research UK (2017) The impact of collaboration: The value of UK medical research to EU science and health

Installation

1. Install package ctrdata in R

Package ctrdata is on CRAN and on GitHub. Within R, use the following commands to install package ctrdata:

# Install CRAN version:
install.packages("ctrdata")

# Alternatively, install development version:
install.packages("devtools")
devtools::install_github("rfhb/ctrdata", build_vignettes = TRUE)

These commands also install the package’s dependencies (jsonlite, httr, curl, clipr, xml2, nodbi, stringi, tibble, lubridate, jqr, dplyr, zip and V8).

2. Script to automatically copy user’s query from web browser

This is optional; it works with all registers supported by ctrdata but is recommended for CTIS because the URL in the web browser does not reflect the parameters the user specified for querying this register.

In the web browser, install the Tampermonkey browser extension, click on “New user script” and then on “Tools”, then enter into “Import from URL” this URL: https://raw.githubusercontent.com/rfhb/ctrdata/master/tools/ctrdataURLcopier.js and last click on “Install”.

The browser extension can be disabled and enabled by the user. When enabled, the URLs to all user’s queries in the registers are automatically copied to the clipboard and can be pasted into the queryterm=... parameter of function ctrLoadQueryIntoDb().

Overview of functions in ctrdata

The functions are listed in the approximate order of use in a user’s workflow (in bold, main functions). See also the package documentation overview.

Databases that can be used with ctrdata

Package ctrdata retrieves trial information and stores it in a database collection, which has to be given as a connection object to parameter con for several ctrdata functions; this connection object is created in slightly different ways for the four supported database backends that can be used with ctrdata as shown in the table. For a speed comparison, see the nodbi documentation.

Besides ctrdata functions below, any such a connection object can equally be used with functions of other packages, such as nodbi (last row in table) or, in case of MongoDB as database backend, mongolite (see vignettes).

Vignettes

• Install R package ctrdata
• Retrieve clinical trial information
• Summarise and analyse clinical trial information

Example workflow

The aim is to download protocol-related trial information and tabulate the trials’ status of conduct.

• Attach package ctrdata:

library(ctrdata)

• See help to get started with ctrdata:

help("ctrdata")

• Information on trial registers that can be used with ctrdata:

help("ctrdata-registers")

• Open registers’ advanced search pages in browser:

ctrOpenSearchPagesInBrowser()

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

• Adjust search parameters and execute search in browser

• When trials of interest are listed in browser, copy the address from the browser’s address bar to the clipboard

• Search used in this example: https://www.clinicaltrialsregister.eu/ctr-search/search?query=cancer&age=under-18&phase=phase-one&status=completed

• Get address from clipboard:

q <- ctrGetQueryUrl()
# * Using clipboard content as register query URL: https://www.clinicaltrialsregister.eu/ctr-search/search?query=cancer&age=under-18&phase=phase-one&status=completed
# * Found search query from EUCTR: query=cancer&age=under-18&phase=phase-one&status=completed

q
#                                                   query-term  query-register
# 1 query=cancer&age=under-18&phase=phase-one&status=completed           EUCTR

🔔 Queries in the trial registers can automatically copied to the clipboard (including for “CTIS”, where the URL does not show the query) using our solution here.

• Retrieve protocol-related information, transform and save to database:

The database collection is specified first, using nodbi (see above for how to specify PostgreSQL, RSQlite, DuckDB or MongoDB as backend, see section Databases); then, trial information is retrieved and loaded into the collection:

# Connect to (or newly create) an SQLite database
# that is stored in a file on the local system:
db <- nodbi::src_sqlite(
  dbname = "some_database_name.sqlite_file",
  collection = "some_collection_name"
)

# Retrieve trials from public register:
ctrLoadQueryIntoDb(
  queryterm = q,
  euctrresults = TRUE,
  con = db
)
# * Found search query from EUCTR: query=cancer&age=under-18&phase=phase-one&status=completed
# * Checking trials in EUCTR...
# Retrieved overview, multiple records of 95 trial(s) from 5 page(s) to be downloaded (estimate: 5 MB)
# (1/3) Downloading trials...
# Note: register server cannot compress data, transfer takes longer (estimate: 30 s)
# Download status: 5 done; 0 in progress. Total size: 7.72 Mb (100%)... done!             
# (2/3) Converting to NDJSON (estimate: 1.7 s)...
# (3/3) Importing records into database...
# = Imported or updated 369 records on 95 trial(s) 
# * Checking results if available from EUCTR for 95 trials: 
# (1/4) Downloading and extracting results (. = data, F = file[s] and data, x = none):
# Download status: 95 done; 0 in progress. Total size: 48.63 Mb (100%)... done!             
# Download status: 19 done; 0 in progress. Total size: 76.48 Kb (308%)... done!             
# Download status: 19 done; 0 in progress. Total size: 76.48 Kb (308%)... done!             
# Download status: 19 done; 0 in progress. Total size: 76.48 Kb (308%)... done!             
# F . . . . F . . F . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
# (2/4) Converting to NDJSON (estimate: 8 s)...
# (3/4) Importing results into database (may take some time)...
# (4/4) Results history: not retrieved (euctrresultshistory = FALSE)
# = Imported or updated results for 76 trials
# No history found in expected format.
# Updated history ("meta-info" in "some_collection_name")

Under the hood, external script euctr2json.sh (in ctrdata/exec) transform EUCTR plain text files, and xml2js via V8 in R transforms CTGOV’s ISRCTN’s XML files, into NDJSON, which is imported into the database collection.

• Analyse

Tabulate the status of trials that are part of an agreed paediatric development program (paediatric investigation plan, PIP). ctrdata functions return a data.frame (or a tibble, if package tibble is loaded).

# Get all records that have values in the fields of interest:
result <- dbGetFieldsIntoDf(
  fields = c(
    "a7_trial_is_part_of_a_paediatric_investigation_plan",
    "p_end_of_trial_status",
    "a2_eudract_number"
  ),
  con = db
)

# Find unique (deduplicated) trial identifiers for trials that have more than
# one record, for example for several EU Member States or in several registers:
uniqueids <- dbFindIdsUniqueTrials(con = db)
# Searching for duplicate trials... 
#  - Getting all trial identifiers (may take some time), 369 found in collection
#  - Finding duplicates among registers' and sponsor ids...
#  - 274 EUCTR _id were not preferred EU Member State record for 95 trials
#  - Keeping 95 / 0 / 0 / 0 / 0 records from EUCTR / CTGOV / CTGOV2 / ISRCTN / CTIS
# = Returning keys (_id) of 95 records in collection "some_collection_name"

# Keep only unique / de-duplicated records:
result <- subset(
  result,
  subset = `_id` %in% uniqueids
)

# Tabulate the selected clinical trial information:
with(
  result,
  table(
    p_end_of_trial_status,
    a7_trial_is_part_of_a_paediatric_investigation_plan
  )
)
#                           a7_trial_is_part_of_a_paediatric_investigation_plan
# p_end_of_trial_status      FALSE TRUE
#                                1    1
#   Completed                   47   20
#   GB - no longer in EU/EEA     1    1
#   Ongoing                      5    1
#   Prematurely Ended            2    2
#   Restarted                    0    1
#   Temporarily Halted           1    1

• Add records from another register (CTGOV) into the same collection

🔔Both the current and classic CTGOV website are supported as of 2023-08-05. The new website and API introduced in July 2023 (https://www.clinicaltrials.gov/) is identified in ctrdata as CTGOV2. The website and API which is now called “classic” (https://classic.clinicaltrials.gov/) is identified in ctrdata as CTGOV, and this is backwards-compatible with queries that were previously retrieved with ctrdata.

Both use the same trial identifier (e.g., NCT01234567) for the same trial. As a consequence, queries for the same trial retrieved using CTGOV or CTGOV2 overwrite any previous record for that trial, whether loaded from CTGOV or CTGOV2. Thus, only a single version (the last retrieved) will be in the collection in the user’s database.

Important differences exist between field names and contents of information retrieved using CTGOV or CTGOV2; see the XML schemas for CTGOV and the REST API for CTGOV2. For more details, call help("ctrdata-registers"). This is one of the reasons why ctrdata handles the situation as if these were two different registers.

• Search used in this example: https://www.clinicaltrials.gov/search?cond=Neuroblastoma&aggFilters=ages:child,results:with,studyType:int

# Retrieve trials from another register:
ctrLoadQueryIntoDb(
  queryterm = "cond=Neuroblastoma&aggFilters=ages:child,results:with,studyType:int",
  register = "CTGOV2",
  con = db
)
# * Appears specific for CTGOV REST API 2.0.0
# * Found search query from CTGOV2: cond=Neuroblastoma&aggFilters=ages:child,results:with,studyType:int
# * Checking trials using CTGOV API 2.0.0.-test, found 89 trials
# (1/3) Downloading in 1 batch(es) (max. 1000 trials each; estimate: 8.9 MB total)
# Download status: 1 done; 0 in progress. Total size: 8.37 Mb (813%)... done!             
# (2/3) Converting to NDJSON...
# (3/3) Importing records into database...
# JSON file #: 1 / 1                               
# = Imported or updated 89 trial(s)
# Updated history ("meta-info" in "some_collection_name")

• Using an example from classic CTGOV: https://classic.clinicaltrials.gov/ct2/results?cond=neuroblastoma&rslt=With&recrs=e&age=0&intr=Drug

# Retrieve trials:
ctrLoadQueryIntoDb(
  queryterm = "https://classic.clinicaltrials.gov/ct2/results?cond=neuroblastoma&rslt=With&recrs=e&age=0&intr=Drug",
  verbose = TRUE,
  con = db
)
# * Appears specific for CTGOV CLASSIC
# * Found search query from CTGOV: cond=neuroblastoma&rslt=With&recrs=e&age=0&intr=Drug
# * Checking trials in CTGOV classic...
# Retrieved overview, records of 56 trial(s) are to be downloaded (estimate: 0.45 MB)
# (1/3) Downloading trial file...
# Download status: 1 done; 0 in progress. Total size: 779.10 Kb (100%)... done!             
# (2/3) Converting to NDJSON (estimate: 2 s)...
# (3/3) Importing records into database...
# = Imported or updated 56 trial(s)                
# Updated history ("meta-info" in "some_collection_name")

• Add records from a third register (ISRCTN) into the same collection

Search used in this example: https://www.isrctn.com/search?q=neuroblastoma

# Retrieve trials from another register:
ctrLoadQueryIntoDb(
  queryterm = "https://www.isrctn.com/search?q=neuroblastoma",
  con = db
)
# * Found search query from ISRCTN: q=neuroblastoma
# * Checking trials in ISRCTN...
# Retrieved overview, records of 9 trial(s) are to be downloaded (estimate: 0.2 MB)
# (1/3) Downloading trial file... 
# Download status: 1 done; 0 in progress. Total size: 93.12 Kb (100%)... done!             
# (2/3) Converting to NDJSON (estimate: 0.05 s)...
# (3/3) Importing records into database...
# = Imported or updated 9 trial(s)                 
# Updated history ("meta-info" in "some_collection_name")

• Add records from a fourth register (CTIS 🔔) into the same collection

Queries in the CTIS search interface can be automatically copied to the clipboard so that a user can paste them into queryterm, see here. As of 2023-11-22, there are 410 trials are publicly accessible in CTIS. See below for how to download documents from CTIS.

# See how many trials are in CTIS publicly accessible:
ctrLoadQueryIntoDb(
  queryterm = "",
  register = "CTIS",
  only.count = TRUE,
  con = db
)
# $n
# [1] 411

# Retrieve trials from another register:
ctrLoadQueryIntoDb(
  queryterm = "https://euclinicaltrials.eu/app/#/search?ageGroupCode=2",
  con = db
)
# * Found search query from CTIS: ageGroupCode=2
# * Checking trials in CTIS...
# (1/5) Downloading trials list . found 44 trials
# (2/5) Downloading and processing part I and parts II... (estimate: 9 Mb)
# Download status: 44 done; 0 in progress. Total size: 8.69 Mb (100%)... done!             
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
# (3/5) Downloading and processing additional data:
# publicevents, summary, layperson, csr, cm, inspections, publicevaluation (estimate: 3 Mb)
# Download status: 83 done; 0 in progress. Total size: 2.71 Mb (100%)... done!             
# 44
# (4/5) Importing records into database...
# (5/5) Updating with additional data: . .         
# = Imported / updated 44 / 44 / 44 records on 44 trial(s)
# Updated history ("meta-info" in "some_collection_name")

allFields <- dbFindFields(".*", db)
# Finding fields in database collection (may take some time)...
# Field names cached for this session.
length(allFields[grepl("CTIS", names(allFields))])
# [1] 1704

allFields[grepl("defer|consideration$", allFields, ignore.case = TRUE)]
#                                                                                            CTIS 
#                                                                           "hasDeferrallApplied" 
#                                                                                            CTIS 
# "publicEvaluation.partIIEvaluationList.partIIRfiConsiderations.rfiConsiderations.consideration" 
#                                                                                            CTIS 
#                       "publicEvaluation.partIRfiConsiderations.rfiConsiderations.consideration" 
#                                                                                            CTIS 
#                  "publicEvaluation.partIRfiConsiderations.rfiConsiderations.part1Consideration" 
#                                                                                            CTIS 
#                  "publicEvaluation.validationRfiConsiderations.rfiConsiderations.consideration" 
#                                                                                            CTIS 
#             "publicEvaluation.validationRfiConsiderations.rfiConsiderations.part1Consideration"  

dbGetFieldsIntoDf("publicEvaluation.partIRfiConsiderations.rfiConsiderations.consideration", db)[1,2]
# publicEvaluation.partIRfiConsiderations.rfiConsiderations.consideration
# In(EX)clusion criteria: An adequate definition of WOCBP or postmenopausal woman 
# is missing and should be added to the protocol. / The rationale for the treatment duration of 7 to 
# 18 weeks cannot be followed. No data are available for this short time period and nivolumab treatment. 
# The shortest duration tested so far in 1 year in adjuvant or maintenance treatment protocol. 
# The sponsor is asked to justify and substantiate his assumption that this treatment duration is 
# adequate with respective data. / E: Information regarding the special clinical conditions for 
# conducting clinical trials with minors, \nsee Article 32 Par. 1 lit e) to g) of Regulation (EU) 
# 536/2014 is missing. Please revise the protocol accordingly. So it is indicated to include the 
# patients older than 18 years first and in case of positive results the planned younger patients 
# could follow.\nStatistical Comment: The statistical analyses are missing in the trial protocol. 
# Biometric adequate is a restriction to descriptive evaluations. A sequential evaluation is 
# recommended (adults first and then children). The protocol has to be amended accordingly / 
# Discontinuation criteria for study subjects and clinical trial termination criteria are missing 
# and have to be added. Please amend. [...]

# use an alternative to dbGetFieldsIntoDf()
allData <- nodbi::docdb_query(src = db, key = db$collection, query = '{"ctrname":"CTIS"}')
# names of top-level data items
sort(names(allData))
#  [1] "_id"                           "ageGroup"                      "applications"                 
#  [4] "authorizationDate"             "authorizedPartI"               "authorizedPartsII"            
#  [7] "coSponsors"                    "ctNumber"                      "ctrname"                      
# [10] "ctStatus"                      "decisionDate"                  "eeaEndDate"                   
# [13] "eeaStartDate"                  "endDateEU"                     "eudraCtInfo"                  
# [16] "gender"                        "hasAmendmentApplied"           "hasDeferrallApplied"          
# [19] "id"                            "initialApplicationId"          "isRmsTacitAssignment"         
# [22] "lastUpdated"                   "memberStatesConcerned"         "mscTrialNotificationsInfoList"
# [25] "primarySponsor"                "publicEvaluation"              "record_last_import"           
# [28] "recruitmentStatus"             "sponsorType"                   "startDateEU"                  
# [31] "submissionDate"                "therapeuticAreas"              "title"                        
# [34] "totalNumberEnrolled"           "totalPartIISubjectCount"       "trialCountries"               
# [37] "trialEndDate"                  "trialGlobalEnd"                "trialPhase"                   
# [40] "trialStartDate" 

format(object.size(allData), "MB")
# [1] "42.9 Mb"

• Analysis across trials

Show cumulative start of trials over time.

# use helper library
library(dplyr)
library(magrittr)
library(tibble)
library(purrr)
library(tidyr)

# get names of all fields / variables in the collaction
length(dbFindFields(".*", con = db))
# [1] 2750

dbFindFields("(start.*date)|(date.*decision)", con = db)
# Using cache of fields.

# - Get trial data
result <- dbGetFieldsIntoDf(
  fields = c(
    "ctrname",
    "record_last_import",
    # CTGOV
    "start_date",
    "overall_status",
    # CTGOV2
    "protocolSection.statusModule.startDateStruct.date",
    "protocolSection.statusModule.overallStatus",
    # EUCTR
    "n_date_of_competent_authority_decision",
    "trialInformation.recruitmentStartDate", # needs above: 'euctrresults = TRUE'
    "p_end_of_trial_status", 
    # ISRCTN
    "trialDesign.overallStartDate",
    "trialDesign.overallEndDate",
    # CTIS
    "authorizedPartI.trialDetails.trialInformation.trialDuration.estimatedRecruitmentStartDate",
    "ctStatus"
  ),
  con = db
)

# - Deduplicate trials and obtain unique identifiers 
#   for trials that have records in several registers
# - Calculate trial start date
# - Calculate simple status for ISRCTN
result %<>% 
  filter(`_id` %in% dbFindIdsUniqueTrials(con = db)) %>% 
  rowwise() %>% 
  mutate(start = max(c_across(matches("(date.*decision)|(start.*date)")), na.rm = TRUE)) %>% 
  mutate(isrctnStatus = if_else(trialDesign.overallEndDate < record_last_import, "Ongoing", "Completed")) %>% 
  ungroup()
  
# - Merge fields from different registers with re-leveling
statusValues <- list(
  "ongoing" = c(
    # EUCTR
    "Recruiting", "Active", "Ongoing", 
    "Temporarily Halted", "Restarted",
    # CTGOV
    "Active, not recruiting", "Enrolling by invitation", 
    "Not yet recruiting", "ACTIVE_NOT_RECRUITING",
    # CTIS
    "Ongoing, recruiting", "Ongoing, recruitment ended", 
    "Ongoing, not yet recruiting", "Authorised, not started"
  ),
  "completed" = c("Completed", "COMPLETED"),
  "other" = c("GB - no longer in EU/EEA", "Trial now transitioned",
              "Withdrawn", "Suspended", "No longer available", 
              "Terminated", "TERMINATED", "Prematurely Ended")
)
result[["state"]] <- dfMergeVariablesRelevel(
  df = result, 
  colnames = c(
    "overall_status",  "p_end_of_trial_status",                            
    "ctStatus", "isrctnStatus"
  ),
  levelslist = statusValues
)

# - Plot example
library(ggplot2)
ggplot(result) + 
  stat_ecdf(aes(x = start, colour = state))
ggsave(
  filename = "man/figures/README-ctrdata_across_registers.png",
  width = 5, height = 3, units = "in"
)

• Result-related trial information

Analyse some simple result details (see this vignette for more examples):

# Get all records that have values in any of the specified fields:
result <- dbGetFieldsIntoDf(
  fields = c(
    "clinical_results.baseline.analyzed_list.analyzed.count_list.count",
    "clinical_results.baseline.group_list.group",
    "clinical_results.baseline.analyzed_list.analyzed.units",
    "number_of_arms",
    "study_design_info.allocation",
    "location",
    "condition"
  ),
  con = db
)

# Mangle to calculate:
# - which columns with values for group counts are not labelled Total
# - what are the numbers in each of the groups etc.
result %<>% 
  mutate(
    which_not_total = lapply(clinical_results.baseline.group_list.group, "[[", 1),
    which_not_total = lapply(which_not_total, function(i) which(i["title"] != "Total")),
    num_participants = lapply(
      clinical_results.baseline.analyzed_list.analyzed.count_list.count, 
      function(i) if (is.data.frame(i[[1]])) i[[1]][2] else i[2])
  ) %>% 
  rowwise() %>% 
  mutate(
    is_randomised = case_when(
      study_design_info.allocation == "Randomized" ~ TRUE,
      study_design_info.allocation == "Non-Randomized" ~ FALSE, 
      number_of_arms == 1L ~ FALSE
    ),
    num_sites = length(sapply(location, "[[", "name")),
    which_not_total = list(if (length(which_not_total)) which_not_total else 1L),
    num_participants = sum(as.integer(num_participants[[1]][which_not_total])),
    num_arms_or_groups = max(number_of_arms, length(which_not_total))
  ) 

# Inspect:
# View(result)

# Example plot:
library(ggplot2)
ggplot(data = result) +
  labs(
    title = "Trials including patients with a neuroblastoma",
    subtitle = "ClinicalTrials.Gov, trials with results"
  ) +
  geom_point(
    mapping = aes(
      x = num_sites,
      y = num_participants,
      size = num_arms_or_groups,
      colour = is_randomised
    )
  ) +
  scale_x_log10() +
  scale_y_log10() +
  labs(
    x = "Number of sites",
    y = "Total number of participants",
    colour = "Randomised?", 
    size = "# Arms / groups")
ggsave(
  filename = "man/figures/README-ctrdata_results_neuroblastoma.png",
  width = 5, height = 3, units = "in"
)

• Download documents: retrieve protocols, statistical analysis plans and other documents into the local folder ./files-.../

### EUCTR document files can be downloaded when results are requested
# All files are downloaded and saved (documents.regexp is not used) 
ctrLoadQueryIntoDb(
  queryterm = "query=cancer&age=under-18&phase=phase-one",
  register = "EUCTR",
  euctrresults = TRUE,
  documents.path = "./files-euctr/",
  con = db
)
# * Found search query from EUCTR: query=cancer&age=under-18&phase=phase-one
# [...]
# Created directory ./files-euctr/
# Downloading trials...
# [...]
# = Imported or updated results for 114 trials
# = documents saved in './files-euctr'


### CTGOV files are downloaded, here corresponding to the default of 
# documents.regexp = "prot|sample|statist|sap_|p1ar|p2ars|ctaletter|^[0-9]+ "
ctrLoadQueryIntoDb(
  queryterm = "cond=Neuroblastoma&type=Intr&recrs=e&phase=1&u_prot=Y&u_sap=Y&u_icf=Y",
  register = "CTGOV",
  documents.path = "./files-ctgov/",
  con = db
)
# * Found search query from CTGOV: cond=Neuroblastoma&type=Intr&recrs=e&phase=1&u_prot=Y&u_sap=Y&u_icf=Y
# [...]
# Downloading documents into 'documents.path' = ./files-ctgov/
# - Created directory ./files-ctgov
# Applying 'documents.regexp' to 14 documents
# Downloading 10 documents:
# Download status: 10 done; 0 in progress. Total size: 38.10 Mb (100%)... done!             
# Newly saved 10 document(s) for 7 trial(s); 0 document(s) for 0 trial(s) already existed


### CTGOV2 files are downloaded, here corresponding to the default of 
# documents.regexp = "prot|sample|statist|sap_|p1ar|p2ars|ctaletter|^[0-9]+ "
ctrLoadQueryIntoDb(
  queryterm = "https://clinicaltrials.gov/search?cond=neuroblastoma&aggFilters=phase:1,results:with",
  documents.path = "./files-ctgov2/",
  con = db
)


### ISRCTN files are downloaded, here corresponding to the default of 
# documents.regexp = "prot|sample|statist|sap_|p1ar|p2ars|ctaletter|^[0-9]+ "
ctrLoadQueryIntoDb(
  queryterm = "https://www.isrctn.com/search?q=alzheimer",
  documents.path = "./files-isrctn/",
  con = db
)
# * Found search query from CTGOV2: cond=neuroblastoma&aggFilters=phase:1,results:with
# [...]
# * Downloading documents into 'documents.path' = ./files-ctgov2/
# - Created directory ./files-ctgov2
# - Creating subfolder for each trial
# - Applying 'documents.regexp' to 30 documents
# - Downloading 26 missing documents
# Download status: 26 done; 0 in progress. Total size: 70.12 Mb (100%)... done!             
# = Newly saved 26 document(s) for 19 trial(s); 0 document(s) for 0 trial(s) already 
# existed in ./files-ctgov2


### CTIS files are downloaded, here corresponding to the default of 
# documents.regexp = "prot|sample|statist|sap_|p1ar|p2ars|ctaletter|^[0-9]+ "
ctrLoadQueryIntoDb(
  queryterm = "https://euclinicaltrials.eu/app/#/search?ageGroupCode=2",
  documents.path = "./files-ctis/",
  con = db
)
# * Found search query from CTIS: ageGroupCode=2
# [...]
# * Downloading documents into 'documents.path' = ./files-ctis/
# - Created directory ./files-ctis
# - Getting ids of lists with document information
# - Downloading 939 lists with document information (estimate: 18.78 Mb)
# Download status: 939 done; 0 in progress. Total size: 12.82 Mb (100%)... done!             
# Download status: 294 done; 0 in progress. Total size: 2.16 Mb (100%)... done!             
# - Processing document information in 939 lists
# - Creating subfolder for each trial
# - Applying 'documents.regexp' to 4733 documents
# - Downloading 440 missing documents
# Download status: 440 done; 0 in progress. Total size: 115.61 Mb (100%)... done!             
# Download status: 211 done; 0 in progress. Total size: 148.06 Mb (100%)... done!             
# Download status: 81 done; 0 in progress. Total size: 130.86 Mb (100%)... done!             
# Download status: 47 done; 0 in progress. Total size: 690 b (100%)... done!             
# = Newly saved 393 document(s) for 39 trial(s) (latest versions only, deduplicated 
# if e.g. in application and authorised part); 0 document(s) for 0 trial(s) already 
# existed in./files-ctis

Tests

See also https://app.codecov.io/gh/rfhb/ctrdata/tree/master/R

tinytest::test_all()
# test_ctrdata_ctrfindactivesubstance.R    4 tests OK 10.1s
# test_ctrdata_mongo_local_ctgov.R   36 tests OK 48.4s
# test_ctrdata_mongo_local_ctgov2.R   16 tests OK 20.6s
# test_ctrdata_mongo_local_ctis.R   23 tests OK 2.2s
# test_ctrdata_mongo_local_euctr.R   73 tests OK 1.4s
# test_ctrdata_mongo_local_isrctn.R   33 tests OK 15.2s
# test_ctrdata_other_functions.R   63 tests OK 2.8s
# test_ctrdata_sqlite_ctgov.R...   36 tests OK 2.8s
# test_ctrdata_sqlite_ctgov2.R..   16 tests OK 1.4s
# test_ctrdata_sqlite_ctis.R....   23 tests OK 3.3s
# test_ctrdata_sqlite_euctr.R...   73 tests OK 6.3s
# test_ctrdata_sqlite_isrctn.R..   33 tests OK 23.8s
# test_euctr_error_sample.R.....    8 tests OK 0.7s
# All ok, 437 results (19m 23.7s)

Future features

• See project outline https://github.com/users/rfhb/projects/1

• Replace external command line tools still needed for EUCTR, CTGOV, ISRCTN, likely by V8 and custom Javascript

• Canonical definitions, filters, calculations are in the works (since August 2023) for data mangling and analyses across registers, e.g. to define study population, identify interventional trials, calculate study duration; public collaboration on these canonical scripts will speed up harmonising analyses.

• Authentication, expected to be required by CTGOV2; specifications not yet known (work not yet started).

• Explore further registers such as ICTRP (authentication needed), JPRN, jRCT, UMIN-CTR, ChiCTR (exploration is continually ongoing; added value, terms and conditions for programmatic access vary; no clear roadmap is established yet).

• Retrieve previous versions of protocol- or results-related information (work not yet started). The challenge is that, apparently, initial versions cannot be queried and historical versions can only be retrieved one-by-one and not in structured format.

• Merge results-related fields retrieved from different registers, such as corresponding endpoints (work not yet started). The challenge is the incomplete congruency and different structure of data fields.

Acknowledgements

• Data providers and curators of the clinical trial registers. Please review and respect their copyrights and terms and conditions, see ctrOpenSearchPagesInBrowser(copyright = TRUE).

• Package ctrdata has been made possible building on the work done for R, clipr. curl, dplyr, duckdb, httr, jqr, jsonlite, lubridate, mongolite, nodbi, RPostgres, RSQLite, rvest, stringi and xml2.

Issues and notes

• Please file issues and bugs here. Also check out how to handle some of the closed issues, e.g. on C stack usage too close to the limit and on a SSL certificate problem: unable to get local issuer certificate

• Information in trial registers may not be fully correct; see for example this publication on CTGOV.

• No attempts were made to harmonise field names between registers (nevertheless, dfMergeVariablesRelevel() can be used to merge and map several variables / fields into one).

Trial records’ JSON in databases

PostgreSQL

MongoDB

SQLite