CRAN status ctrdata status badge codecov R-CMD-CHECK-win-macos-linux-duckdb-mongodb-sqlite-postgres

Main featuresInstallationOverviewDatabasesData modelExample workflowAnalysis across trialsTestsAcknowledgementsFuture

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

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 2024-02-04 for version 1.17.0 (major improvement: removed external dependencies; refactored dbGetFieldsIntoDf()).

Main features

Remember to respect the registers’ terms and conditions (see ctrOpenSearchPagesInBrowser(copyright = TRUE)). Please cite this package in any publication as follows: “Ralf Herold (2024). ctrdata: Retrieve and Analyze Clinical Trials in Public Registers. R package version 1.17.0, https://cran.r-project.org/package=ctrdata”.

References

Package ctrdata has been used for:

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.

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)
dfMergeVariablesRelevel() Merge variables into a new variable, optionally map values to a new set of levels

Databases for use 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).

Purpose Function call
Create SQLite database connection dbc <- nodbi::src_sqlite(dbname = "name_of_my_database", collection = "name_of_my_collection")
Create MongoDB database connection dbc <- nodbi::src_mongo(db = "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 DuckDB database connection dbc <- nodbi::src_duckdb(dbdir = "name_of_my_database", collection = "name_of_my_collection")
Use connection with ctrdata functions ctrdata::{ctrLoadQueryIntoDb, dbQueryHistory, dbFindIdsUniqueTrials, dbFindFields, dbGetFieldsIntoDf}(con = dbc, ...)
Use connection with nodbi functions e.g., nodbi::docdb_query(src = dbc, key = dbc$collection, ...)

Data model of ctrdata

Package ctrdata uses the data models that are implicit in data retrieved from the different registers. No mapping is provided for any register’s data model to a putative target data model. The reasons include that registers’ data models are notably evolving over time and that there are only few data fields with similar values and meaning between the registers.

Thus, the handling of data from different models of registers is to be done at the time of analysis. This approach allows a high level of flexibility, transparency and reproducibility. See examples in the help text for function dfMergeVariablesRelevel() and section Analysis across trials below for how to align related fields from different registers for a joint analysis.

In any backend NoSQL database, one clinical trial is one document (and one row in SQLite, PostgreSQL, DuckDB). The NoSQL backends allow different documents to have different structures. Package ctrdata stores in every such document:

Vignettes

Example workflow

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

library(ctrdata)
help("ctrdata")
help("ctrdata-registers")
ctrOpenSearchPagesInBrowser()

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

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
)
# * Checking trials in EUCTR...
# Retrieved overview, multiple records of 97 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: 7.91 Mb (100%)... done!             
# (2/3) Converting to NDJSON (estimate: 2 s)...
# (3/3) Importing records into database...
# = Imported or updated 377 records on 97 trial(s) 
# * Checking results if available from EUCTR for 97 trials: 
# (1/4) Downloading and extracting results (. = data, F = file[s] and data, x = none):
# Download status: 97 done; 0 in progress. Total size: 48.64 Mb (100%)... done!             
# Download status: 21 done; 0 in progress. Total size: 84.53 Kb (308%)... done!             
# Download status: 21 done; 0 in progress. Total size: 84.53 Kb (308%)... done!             
# Download status: 21 done; 0 in progress. Total size: 84.53 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")
# $n
# [1] 377

Under the hood, EUCTR plain text and XML files from EUCTR, CTGOV, ISRCTN are converted using Javascript via V8 in R into NDJSON, which is imported into the database collection.

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                   48   21
#   GB - no longer in EU/EEA     1    1
#   Ongoing                      5    1
#   Prematurely Ended            2    2
#   Restarted                    0    1
#   Temporarily Halted           1    1

🔔Both the current and classic CTGOV website are supported by ctrdata since 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.

# 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 91 trials
# (1/3) Downloading in 1 batch(es) (max. 1000 trials each; estimate: 9.1 MB total)
# Download status: 1 done; 0 in progress. Total size: 8.80 Mb (829%)... done!             
# (2/3) Converting to NDJSON...
# (3/3) Importing records into database...
# JSON file #: 1 / 1                               
# = Imported or updated 91 trial(s)
# Updated history ("meta-info" in "some_collection_name")
# Retrieve trials:
ctrLoadQueryIntoDb(
  queryterm = "https://classic.clinicaltrials.gov/ct2/results?cond=neuroblastoma&rslt=With&recrs=e&age=0&intr=Drug",
  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...
# (1/3) Downloading trial file...
# Download status: 1 done; 0 in progress. Total size: 812.73 Kb (100%)... done!             
# (2/3) Converting to NDJSON (estimate: 3 s)...
# (3/3) Importing records into database...
# = Imported or updated 58 trial(s)                
# Running dbCTRUpdateQueryHistory...
# Running dbQueryHistory ...
# Number of queries in history of "some_collection_name": 3
# Number of records in collection "some_collection_name": 469
# Updated history ("meta-info" in "some_collection_name")

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")

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 2024-01-06, there are more than 410 trials 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] 464

# 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 46 trials
# (2/5) Downloading and processing part I and parts II... (estimate: 9 Mb)
# Download status: 46 done; 0 in progress. Total size: 9.25 Mb (100%)... done!             
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
# (3/5) Downloading and processing additional data:
# publicevents, summary, layperson, csr, cm, inspections, publicevaluation (estimate: 3 Mb)
# Download status: 95 done; 0 in progress. Total size: 2.93 Mb (100%)... done!             
# 46
# (4/5) Importing records into database...
# (5/5) Updating with additional data: . .         
# = Imported / updated 46 / 46 / 46 records on 46 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] 3183

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] "47.2 Mb"

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] 4788

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"
)
Analysis across registers

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.value",
    "clinical_results.baseline.group_list.group.title",
    "clinical_results.baseline.analyzed_list.analyzed.units",
    "number_of_arms",
    "study_design_info.allocation",
    "location.facility.name",
    "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 %<>% 
  rowwise() %>% 
  mutate(
    is_randomised = case_when(
      study_design_info.allocation == "Randomized" ~ TRUE,
      study_design_info.allocation == "Non-Randomized" ~ FALSE, 
      number_of_arms == 1L ~ FALSE
    ),
    which_not_total = list(which(strsplit(
      clinical_results.baseline.group_list.group.title, " / ")[[1]] != "Total")),
    num_sites = length(strsplit(location.facility.name, " / ")[[1]]),
    num_participants = sum(as.integer(clinical_results.baseline.analyzed_list.analyzed.count_list.count.value[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"
)
Neuroblastoma trials
### 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|ctalett|lay|^[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|ctalett|lay|^[0-9]+ "
ctrLoadQueryIntoDb(
  queryterm = "https://clinicaltrials.gov/search?cond=neuroblastoma&aggFilters=phase:1,results:with",
  documents.path = "./files-ctgov2/",
  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


### ISRCTN files are downloaded, here corresponding to the default of 
# documents.regexp = "prot|sample|statist|sap_|p1ar|p2ars|ctalett|lay|^[0-9]+ "
ctrLoadQueryIntoDb(
  queryterm = "https://www.isrctn.com/search?q=alzheimer",
  documents.path = "./files-isrctn/",
  con = db
)
# * Found search query from ISRCTN: q=alzheimer
# [...]
# * Downloading documents into 'documents.path' = ./files-isrctn/
# - Created directory /Users/ralfherold/Daten/mak/r/emea/ctrdata/files-isrctn
# - Creating subfolder for each trial
# - Applying 'documents.regexp' to 34 documents
# - Downloading 23 missing documents
# Download status: 23 done; 0 in progress. Total size: 10.42 Mb (100%)... done!             
# Download status: 2 done; 0 in progress. Total size: 6.53 Kb (100%)... done!             
# Download status: 2 done; 0 in progress. Total size: 6.53 Kb (100%)... done!             
# Download status: 2 done; 0 in progress. Total size: 6.53 Kb (100%)... done!             
# = Newly saved 21 document(s) for 9 trial(s); 0 document(s) for 0 trial(s) already 
# existed in ./files-isrctn


### CTIS files are downloaded, here corresponding to the default of 
# documents.regexp = "prot|sample|statist|sap_|p1ar|p2ars|ctalett|lay|^[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 2.9s
# test_ctrdata_mongo_local_ctgov.R   51 tests OK 43.5s
# test_ctrdata_mongo_local_ctgov2.R   32 tests OK 38.0s
# test_ctrdata_mongo_local_ctis.R  129 tests OK 7.4s   
# test_ctrdata_mongo_local_euctr.R  98 tests OK 1.1s
# test_ctrdata_mongo_local_isrctn.R   37 tests OK 14.2s
# test_ctrdata_other_functions.R   63 tests OK 2.2s
# test_ctrdata_sqlite_ctgov.R...   51 tests OK 55.0s
# test_ctrdata_sqlite_ctgov2.R..   32 tests OK 44.7s
# test_ctrdata_sqlite_ctis.R....  131 tests OK 6.1s      
# test_ctrdata_sqlite_euctr.R...  98 tests OK 1.0s
# test_ctrdata_sqlite_isrctn.R..   37 tests OK 14.1s
# test_euctr_error_sample.R.....    8 tests OK 0.5s
# All ok, 771 results (14m 34.6s)

Future features

Acknowledgements

Issues and notes

Trial records’ JSON in databases

PostgreSQL

Example JSON representation in PostgreSQL

MongoDB

Example JSON representation in MongoDB

SQLite

Example JSON representation in SQLite