Show simple item record

dc.contributor.authorWong, Chi Heem
dc.contributor.authorSiah, Kien Wei
dc.contributor.authorLo, Andrew W
dc.date.accessioned2020-11-13T21:39:39Z
dc.date.available2020-11-13T21:39:39Z
dc.date.issued2018-01
dc.date.submitted2017-11
dc.identifier.issn1465-4644
dc.identifier.issn1468-4357
dc.identifier.urihttps://hdl.handle.net/1721.1/128480
dc.description.abstractPrevious estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.en_US
dc.publisherOxford University Press (OUP)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1093/BIOSTATISTICS/KXX069en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceOxford University Pressen_US
dc.titleEstimation of clinical trial success rates and related parametersen_US
dc.typeArticleen_US
dc.identifier.citationWong, Chi Heem et al. “Estimation of Clinical Trial Success Rates and Related Parameters.” Biostatistics 20, 2 (January 2018): 273–286 © 2018 Oxford University Pressen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentSloan School of Managementen_US
dc.relation.journalBiostatisticsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-02-22T16:30:21Z
dspace.orderedauthorsWong, Chi Heem; Siah, Kien Wei; Lo, Andrew Wen_US
dspace.embargo.termsNen_US
dspace.date.submission2019-04-04T15:17:04Z
mit.journal.volume20en_US
mit.journal.issue2en_US
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record