Ontology type: schema:ScholarlyArticle Open Access: True
2018-12
AUTHORSAviv Ladanie, Benjamin Speich, Florian Naudet, Arnav Agarwal, Tiago V. Pereira, Francesco Sclafani, Juan Martin-Liberal, Thomas Schmid, Hannah Ewald, John P. A. Ioannidis, Heiner C. Bucher, Benjamin Kasenda, Lars G. Hemkens
ABSTRACTBACKGROUND: The available evidence on the benefits and harms of novel drugs and therapeutic biologics at the time of approval is reported in publicly available documents provided by the US Food and Drug Administration (FDA). We aimed to create a comprehensive database providing the relevant information required to systematically analyze and assess this early evidence in meta-epidemiological research. METHODS: We designed a modular and flexible database of systematically collected data. We identified all novel cancer drugs and therapeutic biologics approved by the FDA between 2000 and 2016, recorded regulatory characteristics, acquired the corresponding FDA approval documents, identified all clinical trials reported therein, and extracted trial design characteristics and treatment effects. Herein, we describe the rationale and design of the data collection process, particularly the organization of the data capture, the identification and eligibility assessment of clinical trials, and the data extraction activities. DISCUSSION: We established a comprehensive database on the comparative effects of drugs and therapeutic biologics approved by the FDA over a time period of 17 years for the treatment of cancer (solid tumors and hematological malignancies). The database provides information on the clinical trial evidence available at the time of approval of novel cancer treatments. The modular nature and structure of the database and the data collection processes allow updates, expansions, and adaption for a continuous meta-epidemiological analysis of novel drugs. The database allows us to systematically evaluate benefits and harms of novel drugs and therapeutic biologics. It provides a useful basis for meta-epidemiological research on the comparative effects of innovative cancer treatments and continuous evaluations of regulatory developments. More... »
PAGES505
http://scigraph.springernature.com/pub.10.1186/s13063-018-2877-z
DOIhttp://dx.doi.org/10.1186/s13063-018-2877-z
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1107112006
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/30231912
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"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1107112006"
]
}
],
"sameAs": [
"https://doi.org/10.1186/s13063-018-2877-z",
"https://app.dimensions.ai/details/publication/pub.1107112006"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T18:25",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8675_00000540.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1186%2Fs13063-018-2877-z"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s13063-018-2877-z'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s13063-018-2877-z'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13063-018-2877-z'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13063-018-2877-z'
This table displays all metadata directly associated to this object as RDF triples.
303 TRIPLES
21 PREDICATES
67 URIs
21 LITERALS
9 BLANK NODES