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. Use with R for the European Union Clinical Trials Register (“EUCTR”, https://www.clinicaltrialsregister.eu/) and ClinicalTrials.gov (“CTGOV”, https://clinicaltrials.gov/). The motivation is to understand trends in design and conduct of trials, their availability for patients and their detailled results. The package is to be used within the R system.

Last reviewed on 2020-10-17 for version 1.4

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 RSQLite, local or remote MongoDB servers, via R package nodbi. 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).

Remember to respect the registers’ terms and conditions (see ctrOpenSearchPagesInBrowser(copyright = TRUE)). Please cite this package in any publication as follows: Ralf Herold (2020). ctrdata: Retrieve and Analyze Clinical Trials in Public Registers. R package version 1.4, https://cran.r-project.org/package=ctrdata

Package ctrdata has been used for:

Blogging on Innovation coming to paediatric research
Report on The impact of collaboration: The value of UK medical research to EU science and health

Installation

1. Install package 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")

These commands also install the package dependencies, which are nodbi, jsonlite, httr, curl, clipr, xml2, rvest.

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

These command line tools are required for ctrLoadQueryIntoDb(), the main function of package ctrdata.

In Linux and macOS (including version 10.15 Catalina), these are usually already installed.

For MS Windows, install cygwin: In R, run ctrdata::installCygwinWindowsDoInstall() for an automated minimal installation into c:\cygwin. Alternatively, install manually cygwin with packages perl, php-jsonc and php-simplexml into c:\cygwin. The installation needs about 160 MB disk space; no administrator credentials needed.

Testing

Once installed, a comprehensive testing can be executed as follows (this will take several minutes):

tinytest::test_package("ctrdata", at_home = TRUE)

Overview of functions in `ctrdata`

The functions are listed in the approximate order of use.

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
`ctrGetQueryUrlFromBrowser()`	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 database
`dbQueryHistory()`	Show the history of queries that were downloaded into the database
`dbFindIdsUniqueTrials()`	Get the identifiers of de-duplicated trials in the database
`dbFindFields()`	Find names of variables (fields) in the database
`dbGetFieldsIntoDf()`	Create a data.frame from trial records in the database with the specified fields
`dfTrials2Long()` 🆕	Transform a 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()` ⏳	Deprecated - 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)

Example workflow

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

Attach package ctrdata:

library(ctrdata)

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
Get address from clipboard:

q <- ctrGetQueryUrlFromBrowser()
# * Found search query from EUCTR.

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:

Under the hood, scripts euctr2json.sh and xml2json.php (in ctrdata/exec) transform EUCTR plain text files and CTGOV XML files to ndjson format, which is imported into the database. The database is specified first, using nodbi (using RSQlite or MongoDB as backend); then, trial information is retrieved and loaded into the database:

# 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)

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)

# Keep only unique / de-duplicated records:
result <- result[ result[["_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 25  14
#   Ongoing                                              5 60  19
#   Prematurely Ended                                    1  6   3
#   Restarted                                            0  1   0
#   Temporarily Halted                                   0  0   1

Add records from another register into the same database

# Retrieve trials from another register:
ctrLoadQueryIntoDb(
  queryterm = "cond=neuroblastoma&rslt=With&recrs=e&age=0&intr=Drug", 
  register = "CTGOV",
  con = db)

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)
# View(result)

# [1.] get counts for arms into data frame
nsubj <- dfName2Value(
  df = result, 
  valuename = "clinical_results.baseline.analyzed_list.analyzed.count_list.count.value"
) 
nsubj <- tapply(
  X = nsubj[["value"]], 
  INDEX = nsubj[["trial_id"]],
  FUN = sum,
  simplify = TRUE
)
nsubj <- data.frame(
  trial_id = names(nsubj),
  nsubj, 
  stringsAsFactors = FALSE,
  row.names = NULL
)

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

# [3.] randomised?
ncon <- dfName2Value(
  df = result, 
  valuename = "study_design_info.allocation"
) 
ncon <- data.frame(
  trial_id = ncon[["trial_id"]], 
  randomised = !is.na(ncon[["value"]]) & ncon[["value"]] == "Randomized",
  stringsAsFactors = FALSE
)

# merge sets
nset <- merge(nsubj, nsite)
nset <- merge(nset, ncon)

# Example plot
library(ggplot2)
ggplot(data = nset) + 
  labs(title = "Neuroblastoma trials with results",
       subtitle = "clinicaltrials.gov") +
  geom_point(
    mapping = aes(
      x = nsite,
      y = nsubj,
      colour = randomised)) + 
  scale_x_log10() + 
  scale_y_log10() 
ggsave(filename = "inst/image/README-ctrdata_results_neuroblastoma.png",
       width = 4, height = 3, units = "in")

Databases

The database connection object con is created by calling nodbi::src_*(), with parameters that are specific to the database (e.g., url) and with a special parameter collection that is used by ctrdata to identify which table or collection in the database to use. Any such connection object can then be used by ctrdata and generic functions of nodbi in a consistent way, as shown in the table:

Purpose	SQLite	MongoDB
Create database connection	`dbc <- nodbi::src_sqlite(dbname = ":memory:", collection = "name_of_my_collection")`	`dbc <- nodbi::src_mongo(db = "name_of_my_database", collection = "name_of_my_collection", url = "mongodb://localhost")`
Use connection with ctrdata functions	`ctrdata::{ctr,db}*(con = dbc)`	`ctrdata::{ctr,db}*(con = dbc)`
Use connection with nodbi functions	`nodbi::docdb_*(src = dbc, key = dbc$collection)`	`nodbi::docdb_*(src = dbc, key = dbc$collection)`

Features in the works

Merge results-related information retrieved from different registers (e.g. corresponding endpoints) for analyses across trials
Explore relevance to retrieve previous versions of protocol- or results-related information

Acknowledgements

Data providers and curators of the clinical trial registers. Please review and respect their copyrights and terms and conditions (ctrOpenSearchPagesInBrowser(copyright = TRUE)).
This package ctrdata has been made possible building on the work done for R, curl, httr, xml2, rvest, mongolite, nodbi, RSQLite and clipr.

Issues and notes

Please file issues and bugs here.
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, dfMergeTwoVariablesRelevel() can be used to merge and map two variables / fields into one).

Trial records’ JSON in databases