The goal of ctdl is to access clinicaltrials.gov data from R.
You can install the development version of ctdl like so:
# install.packages("devtools")
devtools::install_github("bjoleary/ctdl")First, download the most recent clinical trials data:
library(ctdl)
library(fs)
download_directory <- fs::path_temp()
download_data(filepath_data = download_directory)
#> Checking for /var/folders/10/py32gfh16xjf_tx_6htqs6540000gn/T/RtmpjZLp1w directory...
#> Renaming /var/folders/10/py32gfh16xjf_tx_6htqs6540000gn/T/RtmpjZLp1w to /var/folders/10/py32gfh16xjf_tx_6htqs6540000gn/T/RtmpjZLp1w_archive_20230626122613.520003/...
#> Unzipping ...
#> [1] "2023-06-23"Read in the study data:
studies <- read_studies(paste0(download_directory, "/"))
#> Warning: One or more parsing issues, call `problems()` on your data frame for details,
#> e.g.:
#> dat <- vroom(...)
#> problems(dat)
head(studies)
#> # A tibble: 6 × 69
#> nct_id nlm_download_date_de…¹ study_first_submitte…² results_first_submit…³
#> <chr> <chr> <date> <date>
#> 1 NCT02972… <NA> 2016-11-07 NA
#> 2 NCT00007… <NA> 2000-12-29 NA
#> 3 NCT03288… <NA> 2017-09-18 NA
#> 4 NCT00007… <NA> 2000-12-29 NA
#> 5 NCT00112… <NA> 2005-06-02 NA
#> 6 NCT00112… <NA> 2005-06-02 NA
#> # ℹ abbreviated names: ¹nlm_download_date_description,
#> # ²study_first_submitted_date, ³results_first_submitted_date
#> # ℹ 65 more variables: disposition_first_submitted_date <date>,
#> # last_update_submitted_date <date>, study_first_submitted_qc_date <date>,
#> # study_first_posted_date <date>, study_first_posted_date_type <chr>,
#> # results_first_submitted_qc_date <date>, results_first_posted_date <date>,
#> # results_first_posted_date_type <chr>, …Create some search terms and associate them with topics:
search_terms <-
tibble::tribble(
~term, ~topic,
"Activity Monitor", "Wearable",
"Activity Tracker", "Wearable",
"Adaptive Algorithm", "AI/ML",
"Android", "mHealth",
"Apple Heart", "Wearable",
"Apple Watch", "Wearable",
"Apple Women", "mHealth",
"Apple Hearing", "Wearable",
"Artificial Intelligence", "AI/ML",
"Augmented Reality", "Extended Reality",
"Deep Learning", "AI/ML",
"Digital Biomarker", "Digital Biomarker",
"Digital Health", "Digital Health",
"Extended Reality", "Extended Reality",
"Fitbit", "Wearable",
"Fitness Tracker", "Wearable",
"Google", "Google",
"Health Information Tech", "Health IT",
"Health IT", "Health IT",
"in silico", "in silico",
"iPad", "mHealth",
"iPhone", "mHealth",
"Machine Learning", "AI/ML",
"mHealth", "mHealth",
"Mobile App", "mHealth",
"Mobile Health", "mHealth",
"Mobile Medical App", "mHealth",
"Oura", "Wearable",
"Remote Patient Monitoring", "Telemedicine",
"Remote Monitoring", "Telemedicine",
"ResearchKit", "mHealth",
"RPM", "Telemedicine",
"SaMD", "SaMD",
"SiMD", "SiMD",
"Smart Phone", "mHealth",
"Smartphone", "mHealth",
"Smartwatch", "Wearable",
"Software as a Medical Device", "SaMD",
"Software in a Medical Device", "SiMD",
"Tele-Medicine", "Telemedicine",
"Telehealth", "Telemedicine",
"Telemedicine", "Telemedicine",
"Verily", "Verily",
"Virtual Reality", "Extended Reality",
"Accelerometer", "Wearable",
"Wearable Sensor", "Wearable",
"Wristwatch", "Wearable"
) |>
# Add the escaped (\) word boundary term "\\b" to the beginning and end
# of each search term.
dplyr::mutate(regex_term = paste0("\\b", .data$term, "\\b"))We’ll also store some known false positives in a vector titled
known_fp.
known_fp <-
c(
"NCT03189251", "NCT04226612", "NCT00342394", "NCT00905346", "NCT00051363",
"NCT01282814", "NCT04226612", "NCT00342394", "NCT00905346", "NCT00051363",
"NCT01282814", "NCT00829452", "NCT00759954", "NCT00863915", "NCT00475605",
"NCT00649467", "NCT00649805", "NCT00296127", "NCT01512459", "NCT00568152",
"NCT00368719", "NCT01467570", "NCT01027117", "NCT01552252", "NCT01097226",
"NCT01130051", "NCT01123395", "NCT02560077", "NCT02680119", "NCT01445964",
"NCT02799433", "NCT01456507", "NCT03214276", "NCT01514019", "NCT01449786",
"NCT01585519", "NCT01690676", "NCT01649492", "NCT01932112", "NCT02029781",
"NCT01988389", "NCT02044419", "NCT02101112", "NCT02068014", "NCT02159313",
"NCT02160158", "NCT02013856", "NCT02224365", "NCT02340039", "NCT02396615",
"NCT02542033", "NCT02493140", "NCT02974491", "NCT03227874", "NCT02565472",
"NCT03714464", "NCT04116580", "NCT03053986", "NCT02940249", "NCT03593135",
"NCT02856893", "NCT03523403", "NCT03329638", "NCT03381664", "NCT03608319",
"NCT03749031", "NCT03911050", "NCT03802682", "NCT03795324", "NCT03846986",
"NCT04120259", "NCT04004182", "NCT04073719", "NCT01418625", "NCT00474682",
"NCT00219388", "NCT00001330", "NCT00000335", "NCT01221116", "NCT00454974",
"NCT01446965", "NCT01484938", "NCT01448005", "NCT01476722", "NCT01740999",
"NCT01847105", "NCT02201706", "NCT03555123", "NCT03835390"
)Now we’ll filter the studies tibble based on the search terms:
dh_studies <-
find_studies(
data_studies = studies,
search_terms = search_terms,
known_fp = known_fp,
max_topics = 5L
) |>
# Remove any studies with a start date in the future
dplyr::filter(.data$start_date <= lubridate::today())And, finally, we’ll plot the results:
plot_studies(
studies = dh_studies,
description = "Digital Health",
this_year = lubridate::year(lubridate::today()),
search_terms = search_terms
) |>
plot()
This is a hobby project and is built on my own time. It’s not from my employer and is not endorsed by my employer. For that matter, it’s not even endorsed by me. (Also, see the license for some important disclaimers.) It’s not intended for any medical or therapeutic use.