Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/29082
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Trotta, Laura | - |
dc.contributor.author | Kabeya, Yuusuke | - |
dc.contributor.author | BUYSE, Marc | - |
dc.contributor.author | Doffagne, Erik | - |
dc.contributor.author | Venet, David | - |
dc.contributor.author | Desmet, Lieven | - |
dc.contributor.author | BURZYKOWSKI, Tomasz | - |
dc.contributor.author | Tsuburaya, Akira | - |
dc.contributor.author | Yoshida, Kazuhiro | - |
dc.contributor.author | Miyashita, Yumi | - |
dc.contributor.author | Morita, Satoshi | - |
dc.contributor.author | Sakamoto, Junichi | - |
dc.contributor.author | Praveen, Paurush | - |
dc.contributor.author | Oba, Koji | - |
dc.date.accessioned | 2019-09-02T09:27:41Z | - |
dc.date.available | 2019-09-02T09:27:41Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | CLINICAL TRIALS, 16 (5), p. 512-522 | - |
dc.identifier.issn | 1740-7745 | - |
dc.identifier.uri | http://hdl.handle.net/1942/29082 | - |
dc.description.abstract | Background/Aims A risk-based approach to clinical research may include a central statistical assessment of data quality. We investigated the operating characteristics of unsupervised statistical monitoring aimed at detecting atypical data in multicenter experiments. The approach is premised on the assumption that, save for random fluctuations and natural variations, data coming from all centers should be comparable and statistically consistent. Unsupervised statistical monitoring consists of performing as many statistical tests as possible on all trial data, in order to detect centers whose data are inconsistent with data from other centers. Methods We conducted simulations using data from a large multicenter trial conducted in Japan for patients with advanced gastric cancer. The actual trial data were contaminated in computer simulations for varying percentages of centers, percentages of patients modified within each center and numbers and types of modified variables. The unsupervised statistical monitoring software was run by a blinded team on the contaminated data sets, with the purpose of detecting the centers with contaminated data. The operating characteristics (sensitivity, specificity and Youden's J-index) were calculated for three detection methods: one using the p-values of individual statistical tests after adjustment for multiplicity, one using a summary of all p-values for a given center, called the Data Inconsistency Score, and one using both of these methods. Results The operating characteristics of the three methods were satisfactory in situations of data contamination likely to occur in practice, specifically when a single or a few centers were contaminated. As expected, the sensitivity increased for increasing proportions of patients and increasing numbers of variables contaminated. The three methods showed a specificity better than 93% in all scenarios of contamination. The method based on the Data Inconsistency Score and individual p-values adjusted for multiplicity generally had slightly higher sensitivity at the expense of a slightly lower specificity. Conclusions The use of brute force (a computer-intensive approach that generates large numbers of statistical tests) is an effective way to check data quality in multicenter clinical trials. It can provide a cost-effective complement to other data-management and monitoring techniques. | - |
dc.description.sponsorship | The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The present study was supported and carried out jointly by the School of Public Health of the University of Tokyo, Japan, and CluePoints SA, Belgium. The Central Statistical Monitoring approach described in this paper was developed by the International Drug Development Institute (IDDI SA) with partial funding from the Government of Wallonia (Biowin Consortium agreement no. 6741), and implemented in the SMART software (US patent 13/452,338). T.B.'s research was partially funded by the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy). The funding sources had no role in the development or review of the present paper. IAP Research Network P7/06 of the Belgian State, (grant/award number:) Biowin Consortium agreement no. 6741, (grant/award number: 6741) Epidemiological and Clinical Research Information Network (ECRIN), (grant/award number:). | - |
dc.language.iso | en | - |
dc.publisher | SAGE PUBLICATIONS LTD | - |
dc.rights | The Author(s) 2019 Article reuse guidelines: sagepub.com/journals-permissions | - |
dc.subject.other | Data quality | - |
dc.subject.other | central statistical monitoring | - |
dc.subject.other | risk-based monitoring | - |
dc.subject.other | simulations | - |
dc.subject.other | operating characteristics | - |
dc.subject.other | fraud detection | - |
dc.title | Detection of atypical data in multicenter clinical trials using unsupervised statistical monitoring | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 522 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 512 | - |
dc.identifier.volume | 16 | - |
local.format.pages | 11 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | [Trotta, Laura; Doffagne, Erik; Praveen, Paurush] CluePoints SA, Ave Albert Einstein 2a, B-1348 Louvain La Neuve, Belgium. [Kabeya, Yuusuke; Oba, Koji] Univ Tokyo, Dept Biostat, Tokyo, Japan. [Kabeya, Yuusuke] EPS Corp, Tokyo, Japan. [Buyse, Marc] IDDI, San Francisco, CA USA. [Buyse, Marc] CluePoints, Wayne, PA USA. [Venet, David] Univ Brussels, IRIDIA, Brussels, Belgium. [Desmet, Lieven] Univ Louvain, Inst Stat Biostat & Actuarial Sci ISBA, Louvain La Neuve, Belgium. [Burzykowski, Tomasz] IDDI, Louvain La Neuve, Belgium. [Burzykowski, Tomasz] Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat I BioSt, Hasselt, Belgium. [Tsuburaya, Akira] Jizankai Med Fdn, Tsuboi Canc Ctr Hosp, Dept Surg, Koriyama, Fukushima, Japan. [Yoshida, Kazuhiro] Gifu Univ, Grad Sch Med, Dept Surg Oncol, Gifu, Japan. [Miyashita, Yumi; Sakamoto, Junichi] ECRIN, Okazaki, Aichi, Japan. [Morita, Satoshi] Kyoto Univ, Grad Sch Med, Dept Biomed Stat & Bioinformat, Kyoto, Japan. [Sakamoto, Junichi] Tokai Cent Hosp, Kakamigahara, Japan. [Oba, Koji] Univ Tokyo, Interfac Initiat Informat Studies, Tokyo, Japan. | - |
local.publisher.place | LONDON | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.artnr | 1740774519862564 | - |
dc.identifier.doi | 10.1177/1740774519862564 | - |
dc.identifier.isi | 000478323700001 | - |
dc.identifier.eissn | 1740-7753 | - |
local.uhasselt.international | yes | - |
item.fulltext | With Fulltext | - |
item.contributor | Trotta, Laura | - |
item.contributor | Kabeya, Yuusuke | - |
item.contributor | BUYSE, Marc | - |
item.contributor | Doffagne, Erik | - |
item.contributor | Venet, David | - |
item.contributor | Desmet, Lieven | - |
item.contributor | BURZYKOWSKI, Tomasz | - |
item.contributor | Tsuburaya, Akira | - |
item.contributor | Yoshida, Kazuhiro | - |
item.contributor | Miyashita, Yumi | - |
item.contributor | Morita, Satoshi | - |
item.contributor | Sakamoto, Junichi | - |
item.contributor | Praveen, Paurush | - |
item.contributor | Oba, Koji | - |
item.accessRights | Restricted Access | - |
item.fullcitation | Trotta, Laura; Kabeya, Yuusuke; BUYSE, Marc; Doffagne, Erik; Venet, David; Desmet, Lieven; BURZYKOWSKI, Tomasz; Tsuburaya, Akira; Yoshida, Kazuhiro; Miyashita, Yumi; Morita, Satoshi; Sakamoto, Junichi; Praveen, Paurush & Oba, Koji (2019) Detection of atypical data in multicenter clinical trials using unsupervised statistical monitoring. In: CLINICAL TRIALS, 16 (5), p. 512-522. | - |
crisitem.journal.issn | 1740-7745 | - |
crisitem.journal.eissn | 1740-7753 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Detection of atypical data in multicenter clinical trials using unsupervised statistical monitoring.pdf Restricted Access | Published version | 1.99 MB | Adobe PDF | View/Open Request a copy |
SCOPUSTM
Citations
2
checked on Sep 2, 2020
WEB OF SCIENCETM
Citations
10
checked on Apr 22, 2024
Page view(s)
96
checked on Sep 6, 2022
Download(s)
82
checked on Sep 6, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.