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https://rfos.fon.bg.ac.rs/handle/123456789/1542Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.creator | Van Poucke, Sven | |
| dc.creator | Thomeer, Michiel | |
| dc.creator | Heath, John | |
| dc.creator | Vukićević, Milan | |
| dc.date.accessioned | 2023-05-12T11:01:33Z | - |
| dc.date.available | 2023-05-12T11:01:33Z | - |
| dc.date.issued | 2016 | |
| dc.identifier.issn | 1438-8871 | |
| dc.identifier.uri | https://rfos.fon.bg.ac.rs/handle/123456789/1542 | - |
| dc.description.abstract | Despite the accelerating pace of scientific discovery, the current clinical research enterprise does not sufficiently address pressing clinical questions. Given the constraints on clinical trials, for a majority of clinical questions, the only relevant data available to aid in decision making are based on observation and experience. Our purpose here is 3-fold. First, we describe the classic context of medical research guided by Poppers' scientific epistemology of "falsificationism." Second, we discuss challenges and shortcomings of randomized controlled trials and present the potential of observational studies based on big data. Third, we cover several obstacles related to the use of observational (retrospective) data in clinical studies. We conclude that randomized controlled trials are not at risk for extinction, but innovations in statistics, machine learning, and big data analytics may generate a completely new ecosystem for exploration and validation. | en |
| dc.publisher | Jmir Publications, Inc, Toronto | |
| dc.rights | openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.source | Journal of Medical Internet Research | |
| dc.subject | randomized controlled trials | en |
| dc.subject | predictive analytics | en |
| dc.subject | modeling | en |
| dc.subject | ensemble methods | en |
| dc.subject | data mining | en |
| dc.subject | big data | en |
| dc.subject | algorithm | en |
| dc.title | Are Randomized Controlled Trials the (G)old Standard? From Clinical Intelligence to Prescriptive Analytics | en |
| dc.type | article | |
| dc.rights.license | BY | |
| dc.citation.issue | 7 | |
| dc.citation.other | 18(7): - | |
| dc.citation.rank | aM21 | |
| dc.citation.volume | 18 | |
| dc.identifier.doi | 10.2196/jmir.5549 | |
| dc.identifier.fulltext | http://prototype2.rcub.bg.ac.rs/bitstream/id/311/1538.pdf | |
| dc.identifier.pmid | 27383622 | |
| dc.identifier.rcub | conv_1882 | |
| dc.identifier.scopus | 2-s2.0-84989936193 | |
| dc.identifier.wos | 000388495600001 | |
| dc.type.version | publishedVersion | |
| item.cerifentitytype | Publications | - |
| item.fulltext | With Fulltext | - |
| item.grantfulltext | open | - |
| item.openairetype | article | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
| Appears in Collections: | Radovi istraživača / Researchers’ publications | |
This item is licensed under a Creative Commons License
