Table of Contents

Overview

Our package ctrdata is continually developed and offered for the R system since 2015. It facilitates investigating and understanding trends in design and conduct of trials, their availability for participants and using their protocols and results for research and meta-analyses. The package can be used with information and documents available in the

EU Clinical Trials Register (“EUCTR”, https://www.clinicaltrialsregister.eu/)
EU Clinical Trials Information System (“CTIS”, https://euclinicaltrials.eu/)
ClinicalTrials.gov (“CTGOV2” since 2023, “CTGOV” from 2015 to 2024)
ISRCTN Registry (https://www.isrctn.com/)

Its features include,

Protocol- and results-related trial information is easily downloaded, including any trial documents available in registers.
Information is stored as JSON in a document-centric database (DuckDB, PostgreSQL, RSQLite or MongoDB), for fast offline access.
Find active substance synonyms, identify unique (de-duplicated) records across registers, merge and recode fields, easily access deeply-nested fields

Download

Package ctrdata is on CRAN: https://cran.r-project.org/package=ctrdata Within R, package ctrdata can be installed with: install.packages("ctrdata")

Documentation

Start here to find the Reference documentation and several Articles with detailed trial analysis: https://rfhb.github.io/ctrdata/

Support

The preferred way to flag issues is via https://github.com/rfhb/ctrdata/issues. Alternatively, use the comments form at the bottom of this page.

Milestones

Since 2024-06-30, CTIS2 relaunched on 2024-06-17 is supported, and queries to CTGOV (retired on 2024-06-25) are made to work with CTGOV2.
Version 1.18.0 of package ctrdata can retrieve historic versions of studies as structured data from CTGOV2 (example), published on 2024-05-13.
By November 2023, ctrdata was freed from all dependencies on command line tools of the operating system.
The European Union's new Clinical Trials Information System (CTIS) is supported since March 2023.
Since 2019, ctrdata supports several databases (through package nodbi, now maintained by the same author).
Various refactoring efforts, such as factoring out functionality, make downloading more robust, handle nested information, generate data frames.
Results from the EU Clinical Trials Register are supported and imported since 2017 (included from ClinicalTrials.Gov since the beginning).
On 15 September 2015, package ctrdata was first published on CRAN.

Disclaimer

When using package ctrdata, the registers’ terms and conditions need to be respected and are shown with ctrOpenSearchPagesInBrowser(copyright = TRUE).

Please cite package ctrdata in any publication as: “Ralf Herold (2024). ctrdata: Retrieve and Analyze Clinical Trials in Public Registers. R package version 1.18.0, https://cran.r-project.org/package=ctrdata”.

References

Package ctrdata has been used for unpublished work and for:

Sood et al. (2022) Managing the evidence infodemic: Automation approaches used for developing NICE COVID-19 living guidelines. https://doi.org/10.1101/2022.06.13.22276242
Lasch et al. (2022) The Impact of COVID‐19 on the Initiation of Clinical Trials in Europe and the United States. 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

Code example

This covers how to obtain information of trials of interest from all supported registers, for plotting their start and completion over time. For more sophisticated examples, see the Articles under Documentation above.

# Load our package ctrdata from the library
library(ctrdata)

# Connect to (or newly create) an SQLite database
# - See help(nodbi) for how to store in the file
#   system and for connecting with other databases
db <- nodbi::src_sqlite(
  collection = "some_collection_name"
)

# Retrieve trials from public register:
ctrLoadQueryIntoDb(
  queryterm = "https://www.clinicaltrialsregister.eu/ctr-search/search?query=neuroblastoma&phase=phase-three",
  euctrresults = TRUE,
  con = db
)

# Retrieve trials from another register:
ctrLoadQueryIntoDb(
  queryterm = 'searchCriteria={"containAll":","containAny":"neuroblastoma","containNot":"}',
  register = "CTIS",
  con = db
)

# Retrieve trials from another register:
ctrLoadQueryIntoDb(
  queryterm = "https://clinicaltrials.gov/search?cond=Neuroblastoma&aggFilters=ages:child,phase:3,studyType:int",
  con = db
)

# Retrieve trials from another register:
ctrLoadQueryIntoDb(
  queryterm = "https://www.isrctn.com/search?q=neuroblastoma",
  con = db
)

# Names of all fields / variables in the collection:
length(dbFindFields(".*", con = db, sample = FALSE))
# Finding fields in database collection (may take some time)  .  .  .  .  .
# Field names cached for this session.
# [1] 3953

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

# - Get trial data
result <- dbGetFieldsIntoDf(
  fields = c(
    "ctrname",
    "record_last_import",
    # 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
)

# use helper packages for plotting
library(dplyr)
library(tidyr)
library(ggplot2)

# - Deduplicate trials and obtain unique identifiers
#   for trials that have records in several registers
# - Calculate trial start date
# - Calculate simple status for ISRCTN
# - Update end of trial status for EUCTR
result %<>%
  filter(`_id` %in% dbFindIdsUniqueTrials(preferregister = c("CTGOV2", "EUCTR"), 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")) %>%
  mutate(p_end_of_trial_status = if_else(
    is.na(p_end_of_trial_status) & !is.na(n_date_of_competent_authority_decision), "Ongoing", p_end_of_trial_status)) %>%
  ungroup()

# - Merge fields from different registers with re-leveling
statusValues <- list(
  "ongoing" = c(
    # EUCTR
    "Recruiting", "Active", "Ongoing",
    "Temporarily Halted", "Restarted",
    # CTIS
    "Ongoing, recruiting", "Ongoing, recruitment ended",
    "Ongoing, not yet recruiting", "Authorised, not started"
  ),
  "completed" = c(
    "Completed", "COMPLETED", "Ended"),
  "other" = c(
    "GB - no longer in EU/EEA", "Trial now transitioned",
    "Withdrawn", "Suspended", "No longer available",
    "Terminated", "TERMINATED", "UNKNOWN",
    "Prematurely Ended", "Under evaluation")
)
result[["state"]] <- dfMergeVariablesRelevel(
  df = result,
  colnames = c(
    "overall_status", "p_end_of_trial_status",
    "protocolSection.statusModule.overallStatus",
    "ctStatus", "isrctnStatus"
  ),
  levelslist = statusValues
)

# - Plot example
ggplot(result) +
  stat_ecdf(aes(x = start, colour = state)) +
  labs(
    title = "Evolution over time of neuroblastoma phase 3 trials",
    subtitle = "Data from EUCTR, CTIS, ISRCTN, CTGOV2",
    x = "Date of start (proposed or realised)",
    y = "Cumulative proportion of trials",
    colour = "Current status",
    caption = Sys.Date()
  )

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# Load our package ctrdata from the library

library(ctrdata)

# Connect to (or newly create) an SQLite database

# - See help(nodbi) for how to store in the file

# system and for connecting with other databases

db <- nodbi::src_sqlite(

collection = "some_collection_name"

)

# Retrieve trials from public register:

ctrLoadQueryIntoDb(

queryterm = "https://www.clinicaltrialsregister.eu/ctr-search/search?query=neuroblastoma&phase=phase-three",

euctrresults = TRUE,

con = db

)

# Retrieve trials from another register:

ctrLoadQueryIntoDb(

queryterm = 'searchCriteria={"containAll":","containAny":"neuroblastoma","containNot":"}',

con = db

)

# Retrieve trials from another register:

ctrLoadQueryIntoDb(

queryterm = "https://clinicaltrials.gov/search?cond=Neuroblastoma&aggFilters=ages:child,phase:3,studyType:int",

con = db

)

# Retrieve trials from another register:

ctrLoadQueryIntoDb(

queryterm = "https://www.isrctn.com/search?q=neuroblastoma",

con = db

)

# Names of all fields / variables in the collection:

length(dbFindFields(".*", con = db, sample = FALSE))

# Finding fields in database collection (may take some time) . . . . .

# Field names cached for this session.

# [1] 3953

dbFindFields("(start.*date)|(date.*decision)", con = db)

# Using cache of fields.

# - Get trial data

result <- dbGetFieldsIntoDf(

fields = c(

"ctrname",

"record_last_import",

# 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

)

# use helper packages for plotting

library(dplyr)

library(tidyr)

library(ggplot2)

# - Deduplicate trials and obtain unique identifiers

# for trials that have records in several registers

# - Calculate trial start date

# - Calculate simple status for ISRCTN

# - Update end of trial status for EUCTR

result %<>%

filter(`_id` %in% dbFindIdsUniqueTrials(preferregister = c("CTGOV2", "EUCTR"), 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")) %>%

mutate(p_end_of_trial_status = if_else(

is.na(p_end_of_trial_status) & !is.na(n_date_of_competent_authority_decision), "Ongoing", p_end_of_trial_status)) %>%

ungroup()

# - Merge fields from different registers with re-leveling

statusValues <- list(

"ongoing" = c(

# EUCTR

"Recruiting", "Active", "Ongoing",

"Temporarily Halted", "Restarted",

# CTIS

"Ongoing, recruiting", "Ongoing, recruitment ended",

"Ongoing, not yet recruiting", "Authorised, not started"

"completed" = c(

"Completed", "COMPLETED", "Ended"),

"other" = c(

"GB - no longer in EU/EEA", "Trial now transitioned",

"Withdrawn", "Suspended", "No longer available",

"Terminated", "TERMINATED", "UNKNOWN",

"Prematurely Ended", "Under evaluation")

)

result[["state"]] <- dfMergeVariablesRelevel(

df = result,

colnames = c(

"overall_status", "p_end_of_trial_status",

"protocolSection.statusModule.overallStatus",

"ctStatus", "isrctnStatus"

levelslist = statusValues

)

# - Plot example

ggplot(result) +

stat_ecdf(aes(x = start, colour = state)) +

labs(

title = "Evolution over time of neuroblastoma phase 3 trials",

subtitle = "Data from EUCTR, CTIS, ISRCTN, CTGOV2",

x = "Date of start (proposed or realised)",

y = "Cumulative proportion of trials",

colour = "Current status",

caption = Sys.Date()

)

Graph example

This example shows the plot resulting from the script above.

Data model used with ctrdata

Package ctrdata uses the data models that are implicit in data retrieved from the different registers. The approach is further explained here, together with the reasons for this choice. A possible future development is to provide a mapping to a canonical data model, which however does not exist at the moment and will require an international approach and alliance.

Here is the model of data from CTIS for a given trial, as an example of how ctrdata downloads, transforms and stores information from registers:

Clinical trials analyses: ctrdata, a tool for leveraging register data

Overview

Download

Documentation

Support

Milestones

Disclaimer

References

Code example

Graph example

Data model used with ctrdata

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