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”, https://clinicaltrials.gov/)
ISRCTN (https://www.isrctn.com/)

The motivation is to understand trends in design and conduct of trials, their availability for patients and their detailled results. ctrdata is a package for the R system, but other systems and tools can be used with the databases created by it. This README was reviewed on 2022-07-24 for version 1.10.0.9002.

Main features:

Protocol-related trial information is easily retrieved (downloaded): Users define a query in a register’s web interface and then use ctrdata to retrieve in one step all trials resulting from the query. Results-related trial information and personal annotations can be including during retrieval. Synonyms of an active substance can also be found.
Retrieved (downloaded) trial information is transformed and stored in a document-centric database, for fast and offline access. Uses PostgreSQL (🔔new in version 1.9.0), RSQLite or MongoDB as databases, via R package nodbi: see section Databases below. Easily re-run a previous query to update a database.
Analysis can be done with R (using ctrdata convenience functions) or others systems. Unique (de-duplicated) trial records are identified across registers. ctrdata can merge and recode information (fields) and also provides easy access even to deeply-nested fields (new in version 1.4.0).

Remember to respect the registers’ terms and conditions (see ctrOpenSearchPagesInBrowser(copyright = TRUE)). Please cite this package in any publication as follows: “Ralf Herold (2021). ctrdata: Retrieve and Analyze Clinical Trials in Public Registers. R package version 1.10.0, https://cran.r-project.org/package=ctrdata” Package ctrdata has been used for: Blogging on Innovation coming to paediatric research and a Report on 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 dependencies, which are nodbi, jsonlite, httr, curl, clipr, xml2, rvest and stringi.

2. Command line tools `perl`, `sed` and `php` (5.2 or higher)

These are required for ctrLoadQueryIntoDb(), the main function of package ctrdata (see Example workflow); the function also checks if the tools can be used.

For MS Windows, install Cygwin: In R, run ctrdata::installCygwinWindowsDoInstall() for an automated minimal installation. Alternatively, manually install Cygwin with packages perl, php-jsonc and php-simplexml into c:\cygwin. The installation needs about 160 MB disk space; no administrator credentials needed.
In macOS including 11 Big Sur, these are already installed; as alternative and 🔔for macOS 12 Monterey, homebrew can be used: brew install php, which seems to include the libraries required for ctrdata.
In Linux, these are usually already installed; tools for installation vary by distribution (e.g., sudo apt install php-cli php-xml php-json).

Vignettes

Overview of functions in `ctrdata`

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

Function name	Function purpose
`ctrOpenSearchPagesInBrowser()`	Open search pages of registers or execute search in web browser
`ctrFindActiveSubstanceSynonyms()`	Find synonyms and alternative names for an active substance
`ctrGetQueryUrl()`	Import from clipboard the URL of a search in one of the registers
`ctrLoadQueryIntoDb()`	Retrieve (download) or update, and annotate, information on trials from a register and store in a collection in a database
`dbQueryHistory()`	Show the history of queries that were downloaded into the collection
`dbFindIdsUniqueTrials()`	Get the identifiers of de-duplicated trials in the collection
`dbFindFields()`	Find names of variables (fields) in the collection
`dbGetFieldsIntoDf()`	Create a data frame (or tibble) from trial records in the database with the specified fields
`dfTrials2Long()`	Transform the data.frame from `dbGetFieldsIntoDf()` into a long name-value data.frame, including deeply nested fields
`dfName2Value()`	From a long name-value data.frame, extract values for variables (fields) of interest (e.g., endpoints)
`dfMergeTwoVariablesRelevel()`	Merge two simple variables into a new variable, optionally map values to a new set of values
`installCygwinWindowsDoInstall()`	Convenience function to install a Cygwin environment (MS Windows only)

If package dplyr is loaded, a tibble is returned instead of a data.frame.

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 three supported database backends that can be used with ctrdata as shown in the table.

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).

Purpose	Function call
Create SQLite database connection	`dbc <- nodbi::src_sqlite(dbname = "name_of_my_database", collection = "name_of_my_collection")`
Create PostgreSQL database connection	`dbc <- nodbi::src_postgres(dbname = "name_of_my_database"); dbc[["collection"]] <- "name_of_my_collection"`
Create MongoDB database connection	`dbc <- nodbi::src_mongo(db = "name_of_my_database", collection = "name_of_my_collection")`
Use connection with `ctrdata` functions	`ctrdata::{ctr,db}*(con = dbc, ...)`
Use connection with `nodbi` functions	`nodbi::docdb_*(src = dbc, key = dbc$collection, ...)`

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-package")

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

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 or MongoDB as backend); 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")

# See section Databases below
# for MongoDB as alternative

# Retrieve trials from public register:
ctrLoadQueryIntoDb(
  queryterm = q,
  con = db)
# * Found search query from EUCTR: query=cancer&age=under-18&phase=phase-one&status=completed
# (1/3) Checking trials in EUCTR:
# Retrieved overview, multiple records of 66 trial(s) from 4 page(s) to be downloaded
# Checking helper binaries: done
# Downloading trials (4 pages in parallel)...
# Note: register server cannot compress data, transfer takes longer, about 0.4s per trial
# Pages: 4 done, 0 ongoing
# (2/3) Converting to JSON, 248 records converted
# (3/3) Importing JSON records into database...
# = Imported or updated 248 records on 66 trial(s)
# * Updated history ("meta-info" in "some_collection_name")

Under the hood, scripts euctr2json.sh and xml2json.php (in ctrdata/exec) transform EUCTR plain text files and CTGOV as well as ISRCTN XML files to ndjson format, 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):

# 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 trial identifiers for trials that have nore than
# one record, for example for several EU Member States:
uniqueids <- dbFindIdsUniqueTrials(con = db)
# Searching for duplicate trials...
#  - Getting trial ids, 279 found in collection
#  - Finding duplicates among registers' and sponsor ids...
#  - 208 EUCTR _id were not preferred EU Member State record for 71 trials
#  - Keeping 71 records from EUCTR
# = Returning keys (_id) of 71 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      Information not present in EudraCT No Yes
#   Completed                                                 6 32  16
#   GB - no longer in EU/EEA                                  0  7   5
#   Ongoing                                                   0  1   0
#   Prematurely Ended                                         1  2   0
#   Restarted                                                 0  1   0

Add records from another register (CTGOV) into the same collection
Search used in this example: https://clinicaltrials.gov/ct2/results?cond=neuroblastoma&rslt=With&recrs=e&age=0&intr=Drug

# Retrieve trials from another register:
ctrLoadQueryIntoDb(
  queryterm = "cond=neuroblastoma&rslt=With&recrs=e&age=0&intr=Drug",
  register = "CTGOV",
  con = db)
# * Found search query from CTGOV: cond=neuroblastoma&rslt=With&recrs=e&age=0&intr=Drug
# (1/3) Checking trials in CTGOV:
# Retrieved overview, records of 44 trial(s) are to be downloaded
# Checking helper binaries: done
# Downloading: 620 kB
# (2/3) Converting to JSON, 44 records converted
# (3/3) Importing JSON records into database...
# = Imported or updated 43 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
# (1/3) Checking trials in ISRCTN:
# Retrieved overview, records of 9 trial(s) are to be downloaded
# Checking helper binaries: done
# Downloading: 90 kB
# (2/3) Converting to JSON, 9 records converted
# (3/3) Importing JSON records into database...
# = Imported or updated 9 trial(s)
# * Updated history ("meta-info" in "some_collection_name")

Result-related trial information

Analyse some simple result details (see 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",
    "study_design_info.allocation",
    "location"),
  con = db)

# Transform all fields into long name - value format
result <- dfTrials2Long(df = result)
# Total 6386 rows, 12 unique names of variables

# [1.] get counts of subjects for all arms into data frame
# This count is in the group where either its title or its
# description starts with "Total"
nsubj <- dfName2Value(
  df = result,
  valuename = "clinical_results.baseline.analyzed_list.analyzed.count_list.count.value",
  wherename = paste0(
    "clinical_results.baseline.group_list.group.title|",
    "clinical_results.baseline.group_list.group.description"),
  wherevalue = "^Total"
)

# [2.] count number of sites
nsite <- dfName2Value(
  df = result,
  # some ctgov records use
  # location.name, others use
  # location.facility.name
  valuename = "^location.*name$"
)
# count
nsite <- tapply(
  X = nsite[["value"]],
  INDEX = nsite[["_id"]],
  FUN = length,
  simplify = TRUE
)
nsite <- data.frame(
  "_id" = names(nsite),
  nsite,
  check.names = FALSE,
  stringsAsFactors = FALSE,
  row.names = NULL
)

# [3.] randomised?
ncon <- dfName2Value(
  df = result,
  valuename = "study_design_info.allocation"
)

# merge sets
nset <- merge(nsubj, nsite, by = "_id")
nset <- merge(nset, ncon, by = "_id")

# Example plot
library(ggplot2)
ggplot(data = nset) +
  labs(title = "Neuroblastoma trials with results",
       subtitle = "clinicaltrials.gov") +
  geom_point(
    mapping = aes(
      x = nsite,
      y = value.x,
      colour = value.y == "Randomized")) +
  scale_x_log10() +
  scale_y_log10() +
  xlab("Number of sites") +
  ylab("Total number of subjects") +
  labs(colour = "Randomised?")
ggsave(filename = "man/figures/README-ctrdata_results_neuroblastoma.png",
       width = 5, height = 3, units = "in")

Neuroblastoma trials * Retrieve protocols, statistical analysis plans and other documents into a local folder ./files/

# eudract files are downloaded as part of results
ctrLoadQueryIntoDb(
  queryterm = q,
  euctrresults = TRUE, 
  euctrresultspdfpath = "./files/",
  con = db)

# ctgov files can separately be downloaded
sapply(
  unlist(strsplit(
    dbGetFieldsIntoDf(
      fields = "provided_document_section.provided_document.document_url",
      con = db)[[2]], 
    split = " / ")), 
  function(f) download.file(
    f, paste0("./files/", gsub("[/:]", "-", f))))

ctrdata for aggregating and analysing clinical trials

Installation

1. Install package `ctrdata` in R

2. Command line tools `perl`, `sed` and `php` (5.2 or higher)

Vignettes

Overview of functions in `ctrdata`

Databases that can be used with `ctrdata`

Example workflow

Meta

Additional features under consideration

Acknowledgements

Issues and notes

Trial records’ JSON in databases

MongoDB

SQLite

ctrdata for aggregating and analysing clinical trials

Installation

1. Install package ctrdata in R

2. Command line tools perl, sed and php (5.2 or higher)

Vignettes

Overview of functions in ctrdata

Databases that can be used with ctrdata

Example workflow

Meta

Additional features under consideration

Acknowledgements

Issues and notes

Trial records’ JSON in databases

MongoDB

SQLite

1. Install package `ctrdata` in R

2. Command line tools `perl`, `sed` and `php` (5.2 or higher)

Overview of functions in `ctrdata`

Databases that can be used with `ctrdata`