scholarly journals Central statistical monitoring of investigator-led clinical trials in oncology

2020 ◽  
Vol 25 (7) ◽  
pp. 1207-1214 ◽  
Author(s):  
Marc Buyse ◽  
Laura Trotta ◽  
Everardo D. Saad ◽  
Junichi Sakamoto
2020 ◽  
Author(s):  
William J Cragg ◽  
Caroline Hurley ◽  
Victoria Yorke-Edwards ◽  
Sally P Stenning

AbstractBackground/AimsIt is increasingly recognised that reliance on frequent site visits for monitoring clinical trials is inefficient. Regulators and trialists have in recent years encouraged more risk-based monitoring. Risk assessment should take place before a trial begins in order to define the overarching monitoring strategy. It can also be done on an ongoing basis, in order to target sites for monitoring activity. Various methods have been proposed for such prioritisation, often using terms like ‘central statistical monitoring’, ‘triggered monitoring’ or, as in ICH Good Clinical Practice guidance, ‘targeted on-site monitoring’. We conducted a scoping review to identify such methods, to establish if any published methods were supported by adequate evidence to allow wider implementation, and to point the way to future developments in this field of research.MethodsWe used 7 publication databases, 2 sets of methodological conference abstracts and an internet search engine to look for methods for using centrally held trial data to assess site conduct during a trial. We included only reports in English, and excluded reports published before 1996 and reports not directly relevant to our research question. We used reference and citation searches to find additional relevant reports. We extracted data using a pre- defined template. We contacted authors to request additional information about included reports and to check whether reports might be eligible.ResultsWe included 30 reports in our final dataset, of which 21 were peer-reviewed publications. 20 reports described central statistical monitoring methods (of which 7 focussed on detection of fraud or misconduct) and 9 described triggered monitoring methods. 21 reports included some assessment of their methods’ effectiveness. Most commonly this involved exploring the methods’ characteristics using real trial data with no known integrity issues. Of the 21 with some effectiveness assessment, most presented limited or no information about whether or not concerns identified through central monitoring constituted meaningful problems. Some reports commented on cost savings from reduced on-site monitoring, but none gave detailed costings for the development and maintenance of central monitoring methods themselves.ConclusionsOur review identified various proposed methods, some of which could be combined within the same trial. The apparent emphasis on fraud detection may not be proportionate in all trial settings. Although some methods have self-justifying benefits for data cleaning activity, many have limitations that may currently prevent their routine use for targeting trial monitoring activity. The implementation costs, or uncertainty about these, may also be a barrier. We make recommendations for how the evidence-base supporting these methods could be improved.


2013 ◽  
Vol 10 (5) ◽  
pp. 783-806 ◽  
Author(s):  
Amy A Kirkwood ◽  
Trevor Cox ◽  
Allan Hackshaw

Author(s):  
Sylviane de Viron ◽  
Laura Trotta ◽  
Helmut Schumacher ◽  
Hans-Juergen Lomp ◽  
Sebastiaan Höppner ◽  
...  

Abstract Background A central statistical assessment of the quality of data collected in clinical trials can improve the quality and efficiency of sponsor oversight of clinical investigations. Material and Methods The database of a large randomized clinical trial with known fraud was reanalyzed with a view to identifying, using only statistical monitoring techniques, the center where fraud had been confirmed. The analysis was conducted with an unsupervised statistical monitoring software using mixed-effects statistical models. The statistical analyst was unaware of the location, nature, and extent of the fraud. Results Five centers were detected as atypical, including the center with known fraud (which was ranked 2). An incremental analysis showed that the center with known fraud could have been detected after only 25% of its data had been reported. Conclusion An unsupervised approach to central monitoring, using mixed-effects statistical models, is effective at detecting centers with fraud or other data anomalies in clinical trials.


Trials ◽  
2011 ◽  
Vol 12 (S1) ◽  
Author(s):  
Amy A Kirkwood ◽  
Allan Hackshaw

2014 ◽  
Vol 33 (30) ◽  
pp. 5265-5279 ◽  
Author(s):  
L. Desmet ◽  
D. Venet ◽  
E. Doffagne ◽  
C. Timmermans ◽  
T. Burzykowski ◽  
...  

2020 ◽  
Vol 28 ◽  
pp. S460
Author(s):  
A. Morales ◽  
L. Miropolsky ◽  
I. Seagal ◽  
K. Evans ◽  
H. Romero ◽  
...  

2017 ◽  
Vol 9 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Lieven Desmet ◽  
David Venet ◽  
Erik Doffagne ◽  
Catherine Timmermans ◽  
Catherine Legrand ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document