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[Main features](#main-features) • [References](#references) •
[Installation](#installation) •
[Overview](#overview-of-functions-in-ctrdata) • [Data
model](#data-model-of-ctrdata) •
[Databases](#databases-for-use-with-ctrdata) • [Example
workflow](#example-workflow) • [Analysis across
registers](#workflow-across-registers-example) • [Tests,
coverage](#tests-and-coverage) • [Acknowledgements](#acknowledgements) •
[Future](#future-features)

# ctrdata for aggregating and analysing clinical trials

Package `ctrdata` provides functions for retrieving (downloading),
aggregating and analysing clinical trials using information (structured
protocol and result data, as well as documents) from public registers.
It can be used with the

- EU Clinical Trials Register (“EUCTR”,
  <https://www.clinicaltrialsregister.eu/>)
- EU Clinical Trials Information System (“CTIS”,
  <https://euclinicaltrials.eu/>, [example](#workflow-ctis-example))
- ClinicalTrials.gov (“CTGOV2”, <https://clinicaltrials.gov/>,
  [example](#workflow-ctgov-example))
- ISRCTN Registry (“ISRCTN”, <https://www.isrctn.com/>)

Additional registers are being explored. The package facilitates
investigating across registers the design and conduct of trials of
interest, to describe their trends and availability for patients and to
facilitate using their detailed results for research and meta-analyses.
`ctrdata` is a package for the [R](https://www.r-project.org/) system,
but other systems and tools can use the databases created with this
package. This README was reviewed on 2026-08-28 for version 1.26.3.

## Main features

- Trial information is easily found and downloaded: `ctrdata` generates
  queries for all registers or takes a user query in a register’s web
  interface, then `ctrdata` retrieves in one go all protocol- and
  results-related trials data. A
  [script](#2-script-to-automatically-copy-users-query-from-web-browser)
  can automate using the query URL from all registers. Annotations can
  be made when downloading trials, [trial documents](#documents-example)
  and [historic
  versions](https://rfhb.github.io/ctrdata/articles/ctrdata_summarise.html#historic-versions-of-trial-records-and-changes-in-sample-sizes)
  can be downloaded.

- Downloaded trial information is stored in a document-centric database,
  for fast and offline access. Information from different registers can
  be accumulated in a single collection. Uses `RSQLite`, `DuckDB`,
  `PostgreSQL`, `MariaDB` or `MongoDB`, see
  [Databases](#databases-that-can-be-used-with-ctrdata). Interactively
  browse through trial structure and data. Easily re-run a previous
  query to update trial records.

- For analyses, `ctrdata` functions suggest canonical [trial
  concepts](https://rfhb.github.io/ctrdata/reference/ctrdata-trial-concepts.html)
  to simplify analyses across registers, find synonyms of an active
  substance, identify unique (de-duplicated) trial records across all
  registers, merge and recode fields as well as easily access
  deeply-nested fields. Analyses can be done with `R` (see
  [vignette](https://rfhb.github.io/ctrdata/articles/ctrdata_summarise.html))
  and other systems, using the stored `JSON`-structured data.

Respect the registers’ terms and conditions, see
`ctrOpenSearchPagesInBrowser(copyright = TRUE)`. Please cite the package
in any publication or work as follows:

<blockquote>

Herold R (2026). “Aggregating and analysing clinical trials data from
multiple public registers using R package ctrdata.” *Research Synthesis
Methods*, *17*(3), 624–656. ISSN 1759-2879, 1759-2887.
<doi:10.1017/rsm.2025.10061> <https://doi.org/10.1017/rsm.2025.10061>.
or <br/>Herold R (2026-08-28). *ctrdata: Retrieve and Analyze Clinical
Trials Data from Public Registers*. R package version 1.26.2.9000,
<https://cran.r-project.org/package=ctrdata>.
</blockquote>

## References

An introduction to the package, together with worked examples and
technical explanations is in:

- Herold R. Aggregating and analysing clinical trials data from multiple
  public registers using R package ctrdata. Research Synthesis Methods.
  2026;17(3):624-656.
  [doi:10.1017/rsm.2025.10061](https://doi.org/10.1017/rsm.2025.10061)

Package `ctrdata` has been used for unpublished works and these
publications:

<!--
- van Zwet et al. (2026) A Statistical Case for Qualified Scientific Optimism. <https://sites.stat.columbia.edu/gelman/research/unpublished/A_statistical_case_for_qualified_scientific_optimism.pdf>
-->

- Machado et al. (2026) The road for developing new pharmacological
  therapies for Parkinson’s disease: Current trends and targets in
  clinical trials. Journal of Parkinson’s disease
  <https://doi.org/10.1177/1877718X251412671>
- Jong et al. (2025) Experiences with Low-Intervention Clinical Trials –
  the New Category under the European Union Clinical Trials Regulation.
  Clinical Trials <https://doi.org/10.1177/17407745241309293>
- Lopez-Rey et al. (2025) Use of Bayesian Approaches in Oncology
  Clinical Trials: A Cross-Sectional Analysis’. Frontiers in
  Pharmacology <https://doi.org/10.3389/fphar.2025.1548997>
- Russek et al. (2025) Supplementing Single-Arm Trials with External
  Control Arms—Evaluation of German Real-World Data. Clinical
  Pharmacology & Therapeutics <https://doi.org/10.1002/cpt.3684>
- Clinical Studies Sweden (2025) National Summary of Clinical Trials in
  Human Medicines Based on CTIS Data
  [link](https://kliniskastudier.se/english/news-archive/news-archive/2025-11-27-national-summary-of-clinical-trials-in-human-medicines-based-on-ctis-data)
- Alzheimer’s disease Horizon Scanning Report (2024)
  [link](https://www.ema.europa.eu/en/documents/report/alzheimers-disease-eu-horizon-scanning-report_en.pdf)
- Kundu et al. (2024) Analysis of Factors Influencing Enrollment Success
  in Hematology Malignancy Cancer Clinical Trials (2008-2023). Blood
  Meeting Abstracts <https://doi.org/10.1182/blood-2024-207446>
- 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>
- Blog post (2018) Innovation coming to paediatric research
  <https://paediatricdata.eu/innovation-coming-to-paediatric-research/>
- Cancer Research UK (2017) The impact of collaboration: The value of UK
  medical research to EU science and health.
  [link](https://acmedsci.ac.uk/file-download/72060732)
- EMA (2017) Results of juvenile animal studies (JAS) and impact on
  anti-cancer medicine development and use in children [PDF file, p
  34](https://www.ema.europa.eu/en/documents/scientific-guideline/results-juvenile-animal-studies-jas-and-impact-anti-cancer-medicine-development-and-use-children_en.pdf#page=34)

<!--
&#10;
``` r
# find publications using ctrdata
utils::browseURL("https://scholar.google.com/scholar?hl=en&as_sdt=0,5&q=%22ctrdata%22")
&#10;# Ferreira, Francisca Santos (2024)
# Reporte de redes de investigação nos ensaios clínicos pediátricos registados no EudraCT
# https://repositorio.ulisboa.pt/handle/10400.5/101030
&#10;# ctrdata is mentioned but not used in
# Sciannameo et al. (2024) 
# Information Extraction from Medical Case Reports Using OpenAI InstructGPT. 
# Computer Methods and Programs in Biomedicine 
# [https://doi.org/10.1016/j.cmpb.2024.108326](https://doi.org/10.1016/j.cmpb.2024.108326)
```
&#10;-->

## Installation

### 1. Install package `ctrdata` in R

Package `ctrdata` is [on
CRAN](https://cran.r-project.org/package=ctrdata) and [on
GitHub](https://github.com/rfhb/ctrdata). Within
[R](https://www.r-project.org/), use the following commands to install
package `ctrdata`:

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

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

These commands also install the package’s dependencies (`jsonlite`,
`httr2`, `xml2`, `nodbi`, `stringi`, `lubridate`, `jqr`, `dplyr`, `zip`,
`readr`, `rlang`, `htmlwidgets`, `stringdist` and `V8`).

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

Optional; works with all registers supported by `ctrdata` and is
recommended for CTIS so that its URL in the web browser reflects the
user’s parameters for querying this register.

In the web browser, install the [Tampermonkey browser
extension](https://www.tampermonkey.net/), click on “New user script”
and then on “Tools”, enter into “Import from URL” this URL:
[`https://raw.githubusercontent.com/rfhb/ctrdata/master/tools/ctrdataURLcopier.js`](https://raw.githubusercontent.com/rfhb/ctrdata/master/tools/ctrdataURLcopier.js)
and then 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()](https://rfhb.github.io/ctrdata/reference/ctrLoadQueryIntoDb.html).

Additionally, this script retrieves results for `CTIS` when opening
search URLs such as
[`https://euclinicaltrials.eu/ctis-public/search#searchCriteria={"status":[3,4]}`](https://euclinicaltrials.eu/ctis-public/search#searchCriteria=%7B%22status%22:%5B3,4%5D%7D).
After changing the URL in the browser, a “Reload page” is needed to
conduct the search and show results.

## Overview of functions in `ctrdata`

Selected functions are listed in the approximate order of use in a
user’s workflow (in bold, main functions). See all functions in the
[package documentation
overview](https://rfhb.github.io/ctrdata/reference/index.html).

| Function name | Function purpose |
|----|----|
| `ctrGenerateQueries()` | From simple user parameters, generates queries for each register to find trials of interest |
| `ctrOpenSearchPagesInBrowser()` | Open search pages of registers or execute search in web browser |
| `ctrLoadQueryIntoDb()` | Retrieve (download) or update, and annotate, information on trials from a register and store in a collection in a database |
| `ctrShowOneTrial()` | Show full structure and all data of a trial, interactively select fields of interest for `dbGetFieldsIntoDf()` |
| `dbFindFields()` | Find names of variables (fields) in the collection |
| `dbGetFieldsIntoDf()` | Create a data frame (or tibble) from trial records in the database with specific fields and trial concepts of interest calculated across registered, see [trial concepts](https://rfhb.github.io/ctrdata/reference/ctrdata-trial-concepts.html) for 20 trial concepts available |
| `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) |

## Data model of `ctrdata`

Package `ctrdata` uses the data models that are implicit in data as
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 continually evolving over time,
that only few data fields have similar values and meaning between
registers, and that the retrieved public data may not correspond to the
registers’ internal data model. The structure of data for a specific
trial can interactively be inspected and searched using function, see
the section [below](#workflow-data-model).

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. To support analyses,
`ctrdata` (from version 1.21.0) provides functions that calculate
concepts of clinical trials across registers, which are commonly used in
analyses, such as start dates, age groups and statistical tests of
results. See
[help(ctrdata-trial-concepts)](https://rfhb.github.io/ctrdata/reference/ctrdata-trial-concepts.html)
and the section [Analysis across
trials](#workflow-across-registers-example) in the example workflow
below. For further analyses, see examples of function
[dfMergeVariablesRelevel()](https://rfhb.github.io/ctrdata/reference/dfMergeVariablesRelevel.html)
on how to align related fields from different registers for a joint
analysis.

In any of the
[databases](https://rfhb.github.io/ctrdata/index.html#databases-for-use-with-ctrdata),
one clinical trial is one document, corresponding to one row in a
`SQLite`, `PostgreSQL` or `DuckDB` table, and to one document in a
`MongoDB` collection. These `NoSQL` backends allow documents to have
different structures, which is used here to accommodate the different
models of data retrieved from the registers. Package `ctrdata` stores in
every such document:

- field `_id` with the trial identification as provided by the register
  from which it was retrieved
- field `ctrname` with the name of the register (`EUCTR`, `CTGOV`,
  `CTGOV2`, `ISRCTN`, `CTIS`) from which that trial was retrieved
- field `record_last_import` with the date and time when that document
  was last updated using `ctrLoadQueryIntoDb()`
- only for `CTGOV2` and `CTIS`: object `history` with a historic version
  of the trial and with `history_version`, which contains the fields
  `version_number` (starting from 1) and `version_date`
- all original fields as provided by the register for that trial (see
  example in
  [vignette](https://rfhb.github.io/ctrdata/articles/ctrdata_summarise.html#historic-versions-of-trial-records-and-changes-in-sample-sizes))

For visualising the data structure for a trial, see this [vignette
section](https://rfhb.github.io/ctrdata/articles/ctrdata_summarise.html#analysing-nested-fields-such-as-trial-results).

## Databases for use with `ctrdata`

Package `ctrdata` retrieves trial data 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
almost identically for the four database backends supported by
`ctrdata`, as shown in the table. For a speed comparison, see the [nodbi
documentation](https://github.com/ropensci/nodbi#benchmark).

Besides `ctrdata` functions below, such a connection object can be used
with functions of other packages, such as `nodbi` (see 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 **DuckDB** database connection\* | `dbc <- nodbi::src_duckdb(dbdir = "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 **MariaDB** database connection | `dbc <- nodbi::src_mariadb(db = "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, ...)` |

- For DuckDB, the JSON extension is needed which can be permanently
  downloaded as follows:

``` r
# user to specify their directory of choice;
# remember to set option for each new R session
options(duckdb.home = "~/.duckdb")

# load and store in above-mentioned 
# directory; only once to be executed
DBI::dbExecute(duckdb::dbConnect(duckdb::duckdb()), 'INSTALL json;')
```

## Vignettes

- [Install R package
  ctrdata](https://rfhb.github.io/ctrdata/articles/ctrdata_install.html)
- [Retrieve clinical trial
  information](https://rfhb.github.io/ctrdata/articles/ctrdata_retrieve.html)
- [Summarise and analyse clinical trial
  information](https://rfhb.github.io/ctrdata/articles/ctrdata_summarise.html)

## Example workflow

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

- Attach package `ctrdata`:

``` r
library(ctrdata)
```

- See help to get started with `ctrdata`:

``` r
help("ctrdata")
```

- Information on trial registers, their contents and how they can be
  used with `ctrdata` (last updated 2026-01-11):

``` r
help("ctrdata-registers")
```

- Open registers’ advanced search pages in browser:

``` r
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=neuroblastoma&phase=phase-two&age=children#tabs>

- Get address from clipboard:

``` r
q <- ctrGetQueryUrl()
# * Using clipboard content as register query URL: https://www.clinicaltrialsregister.eu/ctr-search/search?query=neuroblastoma&phase=phase-two&age=children
# * Found search query from EUCTR: query=neuroblastoma&phase=phase-two&age=children

q
#                                         query-term query-register
# 1 query=neuroblastoma&phase=phase-two&age=children          EUCTR
```

Queries in the trial registers can automatically copied to the clipboard
(including for “CTIS”, where the URL otherwise does not show the user’s
query), see
[here](#2-script-to-automatically-copy-users-query-from-web-browser).

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

For loading the trial information, first a database collection is
specified, using `nodbi` (see above for how to specify `PostgreSQL`,
`RSQlite`, `DuckDB` or `MongoDB` as backend, see section
[Databases](#databases-that-can-be-used-with-ctrdata)):

``` r
# Connect to (or create) an SQLite database
# stored in a file on the local system:
db <- nodbi::src_sqlite(
  dbname = "database_name.sql",
  collection = "collection_name"
)
```

Then, the trial information is retrieved and loaded into the collection:

``` r
# Retrieve trials from public register EUCTR,
# both protocol- and results-related data:
ctrLoadQueryIntoDb(
  queryterm = q,
  euctrresults = TRUE,
  euctrprotocolsall = FALSE, # new since 2025-07-20, loads single 
  # instead of all available country versions of a trial in EUCTR
  con = db
)
# * Found search query from EUCTR: query=neuroblastoma&phase=phase-two&age=children
# * Checking trials in EUCTR, found 73 trials 
# - Downloading in 4 batch(es) (20 trials each; estimate: 5 s)...
# - Downloading 73 records of 73 trials (estimate: 4 s)...            
# - Converting to NDJSON (estimate: 0.2 s)...                           
# - Importing records into database...
# = Imported or updated 73 records on 73 trial(s)  
# * Checking results if available from EUCTR for 73 trials: 
# - Downloading results...
# - Extracting results (. = data, F = file[s] and data, x = none): . . . F . . .
# F . . F . . . . . . . F . F F F . . . . . . F . . . . 
# - Data found for 34 trials
# - Converting to NDJSON (estimate: 1 s)...
# - Importing 34 results into database (may take some time)...
# - Results history: not retrieved (euctrresultshistory = FALSE)
# = Imported or updated results for 34 trials
# No history found in expected format.
# Updated history ("meta-info" in "collection_name")
# $n
# [1] 73
```

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

- Easily generate queries for each register and add records from several
  registers into the same collection

The same parameters can be used to ask `ctrdata` to generate search
queries that apply to each register, for opening the web interfaces and
for loading the trial data into the collection:

``` r
# Generate queries for each register
queries <- ctrGenerateQueries(
  condition = "neuroblastoma",
  recruitment = "completed",
  phase = "phase 2",
  population = "P"
)

queries
# EUCTR 
# "https://www.clinicaltrialsregister.eu/ctr-search/search?query=neuroblastoma&phase=phase-two&age=children&age=adolescent&age=infant-and-toddler&age=newborn&age=preterm-new-born-infants&age=under-18&status=completed" 
# ISRCTN 
# "https://www.isrctn.com/search?&q=&filters=condition:neuroblastoma,phase:Phase II,ageRange:Child,trialStatus:completed,primaryStudyDesign:Interventional" 
# CTGOV2 
# "https://clinicaltrials.gov/search?cond=neuroblastoma&intr=Drug OR Biological&term=AREA[DesignPrimaryPurpose](DIAGNOSTIC OR PREVENTION OR TREATMENT)&aggFilters=phase:2,ages:child,status:com,studyType:int" 
# CTGOV2expert 
# "https://clinicaltrials.gov/expert-search?term=AREA[ConditionSearch]\"neuroblastoma\" AND (AREA[Phase]\"PHASE2\") AND (AREA[StdAge]\"CHILD\") AND (AREA[OverallStatus]\"COMPLETED\") AND (AREA[StudyType]INTERVENTIONAL) AND (AREA[DesignPrimaryPurpose](DIAGNOSTIC OR PREVENTION OR TREATMENT)) AND (AREA[InterventionSearch](DRUG OR BIOLOGICAL))" 
# CTIS 
# "https://euclinicaltrials.eu/ctis-public/search#searchCriteria={\"medicalCondition\":\"neuroblastoma\",\"trialPhaseCode\":[4],\"ageGroupCode\":[2],\"status\":[5,8]}"   

# Open queries in registers' web interfaces
# Note the regular and expert CTGOV2 query
sapply(queries, ctrOpenSearchPagesInBrowser)

# Load all queries into database collection
result <- lapply(queries, ctrLoadQueryIntoDb, con = db)

sapply(result, "[[", "n")
# EUCTR       ISRCTN       CTGOV2 CTGOV2expert         CTIS 
#   180            0          105          105            2
```

- 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).

``` r
# Get all records that have values in the fields of interest:
result <- dbGetFieldsIntoDf(
  # Field of interest
  fields = c("a7_trial_is_part_of_a_paediatric_investigation_plan"),
  # Trial concepts calculated across registers
  calculate = c("f.statusRecruitment", "f.isUniqueTrial"),
  con = db
)
# To review trial concepts details, call 'help("ctrdata-trial-concepts")'
# Querying database (16 fields)...
# Searching for duplicate trials...                             
# - Getting all trial identifiers (may take some time), 316 found in collection
# - Finding duplicates among registers' and sponsor ids...
# - 114 EUCTR _id were not preferred EU Member State record for 67 trials
# - Unique are 0 / 105 / 0 / 60 / 0 records from CTGOV / CTGOV2 / CTIS / EUCTR / ISRCTN
# = Returning keys (_id) of 165 records in collection "collection_name"

# Tabulate the clinical trial information of interest
with(
  result[result$.isUniqueTrial, ],
  table(
    .statusRecruitment,
    a7_trial_is_part_of_a_paediatric_investigation_plan
  )
)
#                   a7_trial_is_part_of_a_paediatric_investigation_plan
# .statusRecruitment FALSE TRUE
#        ongoing         4    3
#        completed      11    6
#        ended early     7    3
#        other           8    2
```

<div id="workflow-ctgov-example">

</div>

- Queries to CTGOV and CTGOV2

The new website and API introduced in July 2023
(<https://clinicaltrials.gov/>) is supported by `ctrdata` since mid-2023
and identified in `ctrdata` as `CTGOV2`.

On 2024-06-25, `CTGOV` retired the classic website and API used by
`ctrdata` since 2015. To support users, `ctrdata` automatically
translates and redirects queries to the current website. This helps with
automatically updating previously loaded queries
(`ctrLoadQueryIntoDb(querytoupdate = <n>)`), manually migrating queries
and reproducible work on clinical trials information. Going forward,
users are recommended to change to use `CTGOV2` queries.

As regards study data, important differences exist between field names
and contents of information retrieved using `CTGOV` or `CTGOV2`; see the
[schema for study protocols in
`CTGOV`](https://cdn.clinicaltrials.gov/documents/xsd/prs/ProtocolRecordSchema.xsd),
the [schema for study
results](https://cdn.clinicaltrials.gov/documents/xsd/prs/RRSUploadSchema.xsd)
and the [Study Data Structure for
`CTGOV2`](https://clinicaltrials.gov/data-api/about-api/study-data-structure).
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 and will continue to identify the current API as
`register = "CTGOV2"`, to support the analysis stage.

Note that loading trials with `ctrdata` overwrites the previous record
with `CTGOV2` data, whether the previous record was retrieved using
`CTGOV` or `CTGOV2` queries.

Example using a CTGOV query:

``` r
# CTGOV search query URL
q <- "https://classic.clinicaltrials.gov/ct2/results?cond=neuroblastoma&rslt=With&recrs=e&age=0&intr=Drug"

# Open old URL (CTGOV) in current website (CTGOV2):
ctrOpenSearchPagesInBrowser(q)
# Since 2024-06-25, the classic CTGOV servers are no longer available. Package ctrdata has translated the classic CTGOV query URL from this call of function ctrLoadQueryIntoDb(queryterm = ...) into a query URL that works with the current CTGOV2. This is printed below and is also part of the return value of this function, ctrLoadQueryIntoDb(...)$url. This URL can be used with ctrdata functions. Note that the fields and data schema of trials differ between CTGOV and CTGOV2.
# 
# Replace this URL:
# 
# https://classic.clinicaltrials.gov/ct2/results?cond=neuroblastoma&rslt=With&recrs=e&age=0&intr=Drug
# 
# with this URL:
# 
# https://clinicaltrials.gov/search?cond=neuroblastoma&intr=Drug&aggFilters=ages:child,results:with,status:com
# 
# * Found search query from CTGOV2: cond=neuroblastoma&intr=Drug&aggFilters=ages:child,results:with,status:com
# [1] "https://clinicaltrials.gov/search?cond=neuroblastoma&intr=Drug&aggFilters=ages:child,results:with,status:com"

# Count trials
ctrLoadQueryIntoDb(
  queryterm = q,
  con = db,
  only.count = TRUE
)
# $n
# [1] 70
```

<div id="workflow-ctis-example">

</div>

- Queries to CTIS

Queries in the CTIS search interface
(<https://euclinicaltrials.eu/ctis-public/search>) can be automatically
copied to the clipboard so that a user can paste them into `queryterm`,
see
[here](#2-script-to-automatically-copy-users-query-from-web-browser).
Subsequent to the relaunch of CTIS on 2024-07-24, there are now more
than 11,050 trials publicly accessible in CTIS. See
[below](#documents-example) for how to download documents from CTIS.

``` r
# See how many trials are in CTIS publicly accessible:
ctrLoadQueryIntoDb(
  queryterm = "",
  register = "CTIS",
  only.count = TRUE
)
# $n
# [1] 11057

# Trials in therapeutic area neoplasms (ICD C04):
ctrLoadQueryIntoDb(
  queryterm = 'searchCriteria={"therapeuticAreaCode":[4]}',
  register = "CTIS",
  only.count = TRUE
)
# * Found search query from CTIS: searchCriteria={"therapeuticAreaCode":[4]}
# * Checking trials in CTIS, found 3532 trials 
# = Not done (only.count = TRUE): Imported 3532 trial(s)
# $n
# [1] 3622
```

<div id="workflow-data-model">

</div>

- Inspect and search structure of trial information

For a given trial, function
[ctrShowOneTrial()](https://rfhb.github.io/ctrdata/reference/ctrShowOneTrial.html)
enables the user to visualise the hiearchy of fields and contents in the
user’s local web browser, to search for field names and field values,
and to select and copy selected fields’ names for use with function
[dbGetFieldsIntoDf()](https://rfhb.github.io/ctrdata/reference/dbGetFieldsIntoDf.html).

``` r
# This opens a local browser for user interaction.
# If the trial identifier (_id) is not found in the specified 
# collection, it will be retrieved from the relevant register.
ctrShowOneTrial(
  identifier = "2024-518931-12-00",
  con = db
)
```

<figure>
<img
src="https://raw.githubusercontent.com/rfhb/ctrdata/master/docs/reference/figures/ctrdata_ctrShowOneTrial.jpg"
alt="ctrShowOneTrial" />
<figcaption aria-hidden="true">ctrShowOneTrial</figcaption>
</figure>

<div id="workflow-across-registers-example">

</div>

- Analysis across registers

Show cumulative start of trials over time. This uses the calculation of
one of the trial concepts that are implemented in `ctrdata` since
version 1.21.0.

``` r
# explore 20 pre-defined concepts for 
# trial analysis across registers
help("ctrdata-trial-concepts")

# use helper packages
library(dplyr)
library(ggplot2)

# calculate concept across registers
result <- dbGetFieldsIntoDf(
  calculate = c(
    "f.statusRecruitment", 
    "f.startDate"),
  con = db
)

# not using trial concept f.isUniqueTrial
# but using dbFindIdsUniqueTrials() in 
# order to prefer CTIS over EUCTR
result$.isUniqueTrial <- result$`_id` %in% 
  dbFindIdsUniqueTrials(
    preferregister = c("CTGOV2", "CTIS", "EUCTR", "CTGOV", "ISRCTN"),
    con = db
  ) 

# plot concepts
result %>%
  filter(.isUniqueTrial) %>%
  ggplot() +
  stat_ecdf(aes(
    x = .startDate,
    colour = .statusRecruitment
  )) +
  labs(
    title = "Evolution over time of selected 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()
  )

ggsave(
  filename = "man/figures/README-ctrdata_across_registers.png",
  width = 5, height = 3, units = "in"
)
```

<figure>
<img
src="https://raw.githubusercontent.com/rfhb/ctrdata/master/docs/reference/figures/README-ctrdata_across_registers.png"
alt="Analysis across registers" />
<figcaption aria-hidden="true">Analysis across registers</figcaption>
</figure>

- Result-related trial information

Analyse some simple result details, here from CTGOV2 (see this
[vignette](https://rfhb.github.io/ctrdata/articles/ctrdata_summarise.html)
for more examples):

``` r
# use helper packages
library(dplyr)
library(ggplot2)

# calculate concept across registers
result <- dbGetFieldsIntoDf(
  calculate = c(
    "f.numSites",
    "f.sampleSize",
    "f.controlType",
    "f.isUniqueTrial", 
    "f.numTestArmsSubstances"
  ),
  con = db
)
# To review trial concepts details, call 'help("ctrdata-trial-concepts")'
# Querying database (50 fields)...

# plot concept
result %>%
  filter(.isUniqueTrial) %>%
  ggplot() +
  labs(
    title = "Selected trials",
    subtitle = "Patients with a neuroblastoma"
  ) +
  geom_point(
    mapping = aes(
      x = .numSites,
      y = .sampleSize,
      size = .numTestArmsSubstances,
      colour = .controlType
    )
  ) +
  scale_x_log10() +
  scale_y_log10() +
  labs(
    x = "Number of sites",
    y = "Total number of participants",
    colour = "Control",
    size = "# Treatments",
    caption = Sys.Date()
  )

ggsave(
  filename = "man/figures/README-ctrdata_results_neuroblastoma.png",
  width = 5, height = 3, units = "in"
)
```

<figure>
<img
src="https://raw.githubusercontent.com/rfhb/ctrdata/master/docs/reference/figures/README-ctrdata_results_neuroblastoma.png"
alt="Neuroblastoma trials" />
<figcaption aria-hidden="true">Neuroblastoma trials</figcaption>
</figure>

<div id="documents-example">

</div>

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

``` r
### EUCTR document files can be downloaded when results are requested

# All files are downloaded and saved (documents.regexp is not used with EUCTR)
ctrLoadQueryIntoDb(
  queryterm = "query=cancer&age=under-18&phase=phase-one",
  register = "EUCTR",
  euctrresults = TRUE,
  euctrprotocolsall = FALSE, # new since 2025-07-20, loads single 
  # instead of all available country versions of a trial in EUCTR
  documents.path = "./files-euctr/",
  con = db
)
# * Found search query from EUCTR: query=cancer&age=under-18&phase=phase-one
# * Checking trials in EUCTR, found 249 trials 
# - Running with euctrresults = TRUE to download documents
# - Created directory ./files-euctr/
# - Downloading in 13 batch(es) (20 trials each; estimate: 30 MB)
# - Downloading 249 records of 249 trials (estimate: 10 s)             
# - Converting to NDJSON (estimate: 0.7 s)...                             
# - Importing records into database...
# = Imported or updated 249 records on 249 trial(s)
# * Checking results if available from EUCTR for 249 trials: 
# - Downloading results...
# - Downloading documents into 'documents.path' = ./files-euctr
# - Extracting results (. = data, F = file[s] and data, x = none): F . . . . F . . F F . F . . . . F . . . . F . . . . . . . F . F . . F . . . . . . . . F F . . . . . . . . . . . . . . . . . . . F F . F . . . . . . . . . . . . . . . . . . . . . . . . . . F . . . . . . . . . . F F . . . . . . . . . . . . . . F . . . . F . . F . . . F . . . . 
# - Data found for 138 trials
# - Converting to NDJSON (estimate: 4 s)...
# - Importing 138 results into database (may take some time)...
# - Results history: not retrieved (euctrresultshistory = FALSE)
# = Imported or updated results for 138 trials
# = Documents saved in './files-euctr'
# No history found in expected format.
# Updated history ("meta-info" in "collection_name")
# $n
# [1] 249


### CTGOV files are downloaded, here corresponding to the default of
# documents.regexp = "prot|sample|statist|sap_|p1ar|p2ars|icf|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
)
# Since 2024-06-25, the classic CTGOV servers are no longer available. Package ctrdata has translated the classic CTGOV query URL from this call of function ctrLoadQueryIntoDb(queryterm = ...) into a query URL that works with the current CTGOV2. This is printed below and is also part of the return value of this function, ctrLoadQueryIntoDb(...)$url. This URL can be used with ctrdata functions. Note that the fields and data schema of trials differ between CTGOV and CTGOV2. 
# 
# Replace this URL:
# 
# https://classic.clinicaltrials.gov/ct2/results?cond=Neuroblastoma&type=Intr&recrs=e&phase=1&u_prot=Y&u_sap=Y&u_icf=Y
# 
# with this URL:
# 
# https://clinicaltrials.gov/search?cond=Neuroblastoma&aggFilters=phase:2,docs:prot sap icf,studyType:int,status:com
# 
# * Found search query from CTGOV2: cond=Neuroblastoma&aggFilters=phase:2,docs:prot sap icf,studyType:int,status:com
# * Checking trials in CTGOV2, found 35 trials
# - Downloading in 1 batch(es) (max. 1000 trials each; estimate: 3.5 MB total)
# - Load and convert batch 1...
# - Importing records into database...
# JSON file #: 1 / 1                               
# * Checking for documents...
# - Getting links to documents
# - Downloading documents into 'documents.path' = ./files-ctgov/
# - Created directory ./files-ctgov
# - Applying 'documents.regexp' to 45 missing documents
# - Creating subfolder for each trial
# = Newly saved 45 document(s) for 35 trial(s); 0 of such document(s) for 0 trial(s) already existed in ./files-ctgov
# = Imported or updated 35 trial(s)
# Updated history ("meta-info" in "collection_name")
# $n
# [1] 35


### CTGOV2 files are downloaded, using the default of documents.regexp
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
# * Checking trials in CTGOV2, found 42 trials
# - Downloading in 1 batch(es) (max. 1000 trials each; estimate: 4.2 MB total)
# - Load and convert batch 1...
# - Importing records into database...
# JSON file #: 1 / 1                               
# * Checking for documents...
# - Getting links to documents
# - Downloading documents into 'documents.path' = ./files-ctgov2/
# - Created directory ./files-ctgov2
# - Applying 'documents.regexp' to 46 missing documents
# - Creating subfolder for each trial
# = Newly saved 46 document(s) for 29 trial(s); 0 of such document(s) for 0 trial(s) already existed in ./files-ctgov2
# = Imported or updated 42 trial(s)
# Updated history ("meta-info" in "collection_name")
# $n
# [1] 42


### ISRCTN files are downloaded, using the default of documents.regexp
ctrLoadQueryIntoDb(
  queryterm = "https://www.isrctn.com/search?q=alzheimer",
  documents.path = "./files-isrctn/",
  con = db
)
# * Found search query from ISRCTN: q=alzheimer
# * Checking trials in ISRCTN, found 350 trials 
# - Downloading trial file (estimate: 6 MB)...
# - Converting to NDJSON (estimate: 2 s)...
# - Importing records into database...
# * Checking for documents...                      
# - Getting links to documents from data . correct with web pages . . . . . . . . 
# - Downloading documents into 'documents.path' = ./files-isrctn/
# - Created directory ./files-isrctn
# - Applying 'documents.regexp' to 61 missing documents
# - Creating subfolder for each trial
# = Newly saved 37 document(s) for 16 trial(s); 0 of such document(s) for 0 trial(s) already existed in ./files-isrctn
# = Imported or updated 350 trial(s)
# Updated history ("meta-info" in "collection_name")
# $n
# [1] 350


### CTIS files are downloaded, using a specific documents.regexp
ctrLoadQueryIntoDb(
  queryterm = paste0(
    "https://euclinicaltrials.eu/ctis-public/search#",
    'searchCriteria={"containAny":"cancer","status":[8]}'),
  documents.path = "./files-ctis/",
  documents.regexp = "^Prtcl-Extrct",
  con = db
)
# * Found search query from CTIS: searchCriteria={"containAny":"cancer","status":[8]}
# * Checking trials in CTIS, found 440 trials 
# - Downloading and processing trial list... (estimate: 0.8 s)
# - Downloading and processing trial data... (estimate: 60 MB)        
# - Importing records into database...                                     
# - Updating with additional data: .               
# * Checking for documents . . . . .  
# - Downloading documents into 'documents.path' = ./files-ctis/
# - Created directory ./files-ctis
# - Applying 'documents.regexp' to 8125 missing documents
# - Creating subfolder for each trial
# - Downloading 2 missing documents . . 
# = Newly saved 2 document(s) for 2 trial(s); 0 of such document(s) for 0 trial(s) already existed in ./files-ctis
# = Imported 440, updated 440 record(s) on 440 trial(s)
# Updated history ("meta-info" in "collection_name")
# $n
# [1] 440
#
# explore types of documents
ctrLoadQueryIntoDb(
  queryterm = paste0(
    "https://euclinicaltrials.eu/ctis-public/search#",
    'searchCriteria={"containAny":"cancer","status":[8]}'),
  documents.path = "./files-ctis/",
  documents.regexp = NULL,
  con = db
)
#
# get names of document files
docNames <- dir(
  path = "./files-ctis/", 
  recursive = TRUE)
# get type of document
docNames <- sub(".+/(.+?) - .+", "\\1", docNames)
# 
table(docNames)
# ClnclStdyRpr LyprsnsSmmoR     Protocol Prtcl-Extrct RcrtmntArrng 
#           48          538         1800            2         1634 
# SbjctIaICF-E SbjctInfaICF SmmryofPrdcC SmmryofRslts SynpssofthPr 
#            2         5595          495          176         1516 
```

## Tests and coverage

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

``` r
# 2026-07-19
tt <- tinytest::test_all()
tt; summary(tt)
# 
# All ok, 618 results (13m 9.1s)
#
# File                                         Results fails passes
#   test_ctrdata_duckdb_ctgov2.R                    80     0     80
#   test_ctrdata_function_activesubstance.R          4     0      4
#   test_ctrdata_function_ctrgeneratequeries.R      10     0     10
#   test_ctrdata_function_params.R                  25     0     25
#   test_ctrdata_function_trial-concepts.R          94     0     94
#   test_ctrdata_function_various.R                 89     0     89
#   test_ctrdata_mongo_local_euctr.R               116     0    116
#   test_ctrdata_mongo_remote_ro.R                   4     0      4
#   test_ctrdata_sqlite_ctgov.R                     46     0     46
#   test_ctrdata_sqlite_ctis.R                      95     0     95
#   test_ctrdata_sqlite_isrctn.R                    45     0     45
#   test_euctr_error_sample.R                       10     0     10
#   Total                                          618     0    618
```

``` r
# 2026-07-19
cp <- covr::package_coverage(path = ".", type = "tests")
covr::report(cp); cp
# ctrdata Coverage: 94.35%
# R/ctrShowOneTrial.R: 84.21%
# R/ctrLoadQueryIntoDbEuctr.R: 85.97%
# R/ctrGetQueryUrl.R: 88.19%
# R/ctrFindActiveSubstanceSynonyms.R: 88.89%
# R/dbGetFieldsIntoDf.R: 89.54%
# R/ctrLoadQueryIntoDbCtis.R: 90.00%
# R/f_primaryEndpointResults.R: 90.08%
# R/util_functions.R: 90.08%
# R/f_sponsorType.R: 91.00%
# R/ctrRerunQuery.R: 92.22%
# R/ctrLoadQueryIntoDbCtgov2.R: 92.70%
# R/zzz.R: 92.86%
# R/ctrLoadQueryIntoDbIsrctn.R: 93.72%
# R/dbFindFields.R: 95.88%
# R/dfMergeVariablesRelevel.R: 96.55%
# R/ctrGenerateQueries.R: 97.16%
# R/ctrOpenSearchPagesInBrowser.R: 97.50%
# R/f_likelyPlatformTrial.R: 98.78%
# R/dbFindIdsUniqueTrials.R: 98.82%
# R/f_numTestArmsSubstances.R: 98.95%
# R/f_externalLinks.R: 99.10%
# R/ctrLoadQueryIntoDb.R: 100.00%
# R/dbQueryHistory.R: 100.00%
# R/dfName2Value.R: 100.00%
# R/dfTrials2Long.R: 100.00%
# R/f_assignmentType.R: 100.00%
# R/f_controlType.R: 100.00%
# R/f_hasResults.R: 100.00%
# R/f_isMedIntervTrial.R: 100.00%
# R/f_isUniqueTrial.R: 100.00%
# R/f_numSites.R: 100.00%
# R/f_primaryEndpointDescription.R: 100.00%
# R/f_resultsDate.R: 100.00%
# R/f_sampleSize.R: 100.00%
# R/f_startDate.R: 100.00%
# R/f_statusRecruitment.R: 100.00%
# R/f_trialObjectives.R: 100.00%
# R/f_trialPhase.R: 100.00%
# R/f_trialPopulation.R: 100.00%
# R/f_trialTitle.R: 100.00%
```

## Future features

- See project outline <https://github.com/users/rfhb/projects/1>

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

- Explore further registers (exploration is continually ongoing; added
  value, terms and conditions for programmatic access vary; no clear
  roadmap is established yet).

## Acknowledgements

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

- Please cite the package in any publication or work as follows as:
  Herold R (2026). “Aggregating and analysing clinical trials data from
  multiple public registers using R package ctrdata.” *Research
  Synthesis Methods*, *17*(3), 624–656. ISSN 1759-2879, 1759-2887.
  <doi:10.1017/rsm.2025.10061> <https://doi.org/10.1017/rsm.2025.10061>.

- Package `ctrdata` has been made possible building on the work done for
  [R](https://www.r-project.org/),
  [dplyr](https://cran.r-project.org/package=dplyr),
  [duckdb](https://cran.r-project.org/package=duckdb),
  [htmlwidgets](https://cran.r-project.org/package=htmlwidgets),
  [httr2](https://cran.r-project.org/package=httr2),
  [jqr](https://cran.r-project.org/package=jqr),
  [jsonlite](https://cran.r-project.org/package=jsonlite),
  [lubridate](https://cran.r-project.org/package=lubridate),
  [mongolite](https://cran.r-project.org/package=mongolite),
  [nodbi](https://cran.r-project.org/package=nodbi),
  [readr](https://cran.r-project.org/package=readr).
  [rlang](https://cran.r-project.org/package=rlang),
  [RPostgres](https://cran.r-project.org/package=RPostgres),
  [RSQLite](https://CRAN.R-project.org/package=RSQLite),
  [stringdist](https://cran.r-project.org/package=stringdist) and
  [stringi](https://cran.r-project.org/package=stringi) and
  [tidyr](https://cran.r-project.org/package=tidyr),
  [V8](https://cran.r-project.org/package=V8),
  [xml2](https://cran.r-project.org/package=xml2).

## Issues and notes

- Information in trial registers may not be fully correct; see for
  example [this publication on
  CTGOV](https://doi.org/10.1136/bmj.k1452).

- A warning may be issued and a record not imported if the complexity of
  the XML content is too high for processing. The issue can be resolved
  by increasing in the operating system the stack size available to R,
  see: <https://github.com/rfhb/ctrdata/issues/22>

- Please file issues and bugs
  [here](https://github.com/rfhb/ctrdata/issues).

- Check out any relevant closed issues, e.g. on [C stack usage too close
  to the limit](https://github.com/rfhb/ctrdata/issues/22) and on a [SSL
  certificate problem: unable to get local issuer
  certificate](https://github.com/rfhb/ctrdata/issues/19#issuecomment-820127139).
