This R data package contains observation, summary, and model-level data from pediatric drug safety research developed by Nicholas Giangreco for his PhD dissertation in the Tatonetti lab at Columbia University.
The database is downloaded after consent is given when using the package. Installing the package will not download the database, but it will make it easier to download and connect to the database from your R session.
install.packages('kidsides')
remotes::install_github("ngiangre/kidsides")The database is comprised of 17 tables including a table with
descriptions of the fields in each table. The main table, ade_nichd,
contains quantitative data from nearly 500,000 pediatric drug safety
signals across 7 child development stages spanning from birth through
late adolescence (21 years of age).
The database was created using the methods and analyses in the references.
This data resource can be used under the CC BY 4.0 license agreement.
See the Overview vignette for more details on the data and the
online portal
library(kidsides)
kidsides::download_sqlite_db(force=TRUE)## kidsides would like to download a 0.9GB 'sqlite' database to your cache. Is that okay?
## The file will be located at at: /Users/nickgiangreco/Library/Caches/org.R-project.R/R/kidsides
## (Yes/no/cancel)
con <- kidsides::connect_sqlite_db()
DBI::dbListTables(con)## [1] "ade"
## [2] "ade_nichd"
## [3] "ade_nichd_enrichment"
## [4] "ade_null"
## [5] "ade_null_distribution"
## [6] "ade_raw"
## [7] "atc_raw_map"
## [8] "cyp_gene_expression_substrate_risk_information"
## [9] "dictionary"
## [10] "drug"
## [11] "drug_gene"
## [12] "event"
## [13] "gene"
## [14] "gene_expression"
## [15] "grip"
## [16] "ryan"
## [17] "sider"
library(dplyr)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
dplyr::tbl(con,"dictionary") %>%
dplyr::collect()## # A tibble: 152 × 4
## table field description type
## <chr> <chr> <chr> <chr>
## 1 drug atc_concept_id The ATC 5th level OMOP concept identifier. int
## 2 drug atc_concept_name The ATC 5th level OMOP concept name. In the ad… char…
## 3 drug atc_concept_code The ATC 5th level OMOP concept code. char…
## 4 drug ndrugreports The number of reports of the drug in Pediatric… int
## 5 drug atc4_concept_name The ATC 4th level OMOP concept name. char…
## 6 drug atc4_concept_code The ATC 4th level OMOP concept code. char…
## 7 drug atc3_concept_name The ATC 3rd level OMOP concept name. char…
## 8 drug atc3_concept_code The ATC 3rd level OMOP concept code. char…
## 9 drug atc2_concept_name The ATC 2nd level OMOP concept name. char…
## 10 drug atc2_concept_code The ATC 2nd level OMOP concept code. char…
## # … with 142 more rows
dplyr::tbl(con,"ade_nichd") %>%
dplyr::collect()## # A tibble: 3,225,859 × 13
## atc_c…¹ meddr…² ade nichd gam_s…³ norm gam_s…⁴ gam_s…⁵ gam_s…⁶ D E
## <int> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 1588648 3.58e7 1588… term… -0.131 0 2.48 -4.21 3.95 0 20
## 2 1588648 3.58e7 1588… infa… 0.947 0.166 1.98 -2.31 4.21 0 80
## 3 1588648 3.58e7 1588… todd… 2.03 0.332 1.79 -0.923 4.98 0 107
## 4 1588648 3.58e7 1588… earl… 3.11 0.499 1.86 0.0553 6.17 0 294
## 5 1588648 3.58e7 1588… midd… 4.21 0.667 2.10 0.745 7.67 0 1046
## 6 1588648 3.58e7 1588… earl… 5.30 0.834 2.52 1.15 9.44 1 2697
## 7 1588648 3.58e7 1588… late… 6.38 1 3.14 1.21 11.5 0 1729
## 8 1588648 3.63e7 1588… term… -0.310 0 5.61 -9.53 8.91 0 0
## 9 1588648 3.63e7 1588… infa… 2.25 0.166 4.43 -5.05 9.54 0 2
## 10 1588648 3.63e7 1588… todd… 4.81 0.332 3.79 -1.43 11.1 0 6
## # … with 3,225,849 more rows, 2 more variables: DE <int>, ade_name <chr>, and
## # abbreviated variable names ¹atc_concept_id, ²meddra_concept_id, ³gam_score,
## # ⁴gam_score_se, ⁵gam_score_90mse, ⁶gam_score_90pse
dplyr::tbl(con,"ade") %>%
dplyr::collect()## # A tibble: 460,823 × 9
## ade atc_c…¹ meddr…² clust…³ gt_nu…⁴ gt_nu…⁵ max_s…⁶ clust…⁷ ade_n…⁸
## <chr> <int> <int> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 1588648_3580… 1588648 3.58e7 2 1 0 late_a… Increa… 1
## 2 1588648_3631… 1588648 3.63e7 2 1 1 late_a… Increa… 1
## 3 1588648_3641… 1588648 3.64e7 2 1 0 late_a… Increa… 1
## 4 1588648_3701… 1588648 3.70e7 2 1 0 late_a… Increa… 1
## 5 1588648_3701… 1588648 3.70e7 2 1 1 late_a… Increa… 1
## 6 1588648_3752… 1588648 3.75e7 2 1 0 late_a… Increa… 1
## 7 1588697_3510… 1588697 3.51e7 4 0 0 term_n… Decrea… 1
## 8 1588697_3510… 1588697 3.51e7 2 0 0 late_a… Increa… 1
## 9 1588697_3510… 1588697 3.51e7 2 0 0 late_a… Increa… 3
## 10 1588697_3510… 1588697 3.51e7 4 0 0 term_n… Decrea… 1
## # … with 460,813 more rows, and abbreviated variable names ¹atc_concept_id,
## # ²meddra_concept_id, ³cluster_id, ⁴gt_null_statistic, ⁵gt_null_99,
## # ⁶max_score_nichd, ⁷cluster_name, ⁸ade_nreports
dplyr::tbl(con,"ade_raw") %>%
dplyr::collect()## # A tibble: 2,326,383 × 23
## safetyr…¹ ade atc_c…² meddr…³ nichd sex repor…⁴ recei…⁵ XA XB XC
## <chr> <chr> <int> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 10003357 2160… 2.16e7 3.67e7 midd… Male Other … 2014-0… 0 0 0
## 2 10003357 2160… 2.16e7 4.29e7 midd… Male Other … 2014-0… 0 0 0
## 3 10003357 2160… 2.16e7 3.67e7 midd… Male Other … 2014-0… 0 0 0
## 4 10003357 2160… 2.16e7 3.67e7 midd… Male Other … 2014-0… 0 0 0
## 5 10003357 2160… 2.16e7 4.29e7 midd… Male Other … 2014-0… 0 0 0
## 6 10003357 2160… 2.16e7 3.67e7 midd… Male Other … 2014-0… 0 0 0
## 7 10003388 2160… 2.16e7 3.52e7 late… Fema… Consum… 2014-0… 0 0 1
## 8 10003388 2160… 2.16e7 3.52e7 late… Fema… Consum… 2014-0… 0 0 1
## 9 10003388 2160… 2.16e7 3.58e7 late… Fema… Consum… 2014-0… 0 0 1
## 10 10003401 2160… 2.16e7 3.61e7 earl… Fema… Consum… 2014-0… 0 0 1
## # … with 2,326,373 more rows, 12 more variables: XD <dbl>, XG <dbl>, XH <dbl>,
## # XJ <dbl>, XL <dbl>, XM <dbl>, XN <dbl>, XP <dbl>, XR <dbl>, XS <dbl>,
## # XV <dbl>, polypharmacy <int>, and abbreviated variable names
## # ¹safetyreportid, ²atc_concept_id, ³meddra_concept_id,
## # ⁴reporter_qualification, ⁵receive_date
kidsides::disconnect_sqlite_db(con)Giangreco, Nicholas. Mind the developmental gap: Identifying adverse drug effects across childhood to evaluate biological mechanisms from growth and development. 2022. Columbia University, PhD dissertation.
Giangreco NP, Tatonetti NP. A database of pediatric drug effects to evaluate ontogenic mechanisms from child growth and development. Med (N Y). 2022 Aug 12;3(8):579-595.e7. doi: 10.1016/j.medj.2022.06.001. Epub 2022 Jun 24. PMID: 35752163; PMCID: PMC9378670.