๐”– Bobbio Scriptorium
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The role of biostatistics in the prevention, detection and treatment of fraud in clinical trials

โœ Scribed by Marc Buyse; Stephen L. George; Stephen Evans; Nancy L. Geller; Jonas Ranstam; Bruno Scherrer; Emmanuel Lesaffre; Gordon Murray; Lutz Edler; Jane Hutton; Theodore Colton; Peter Lachenbruch; Babu L. Verma


Publisher
John Wiley and Sons
Year
1999
Tongue
English
Weight
126 KB
Volume
18
Category
Article
ISSN
0277-6715

No coin nor oath required. For personal study only.

โœฆ Synopsis


Recent cases of fraud in clinical trials have attracted considerable media attention, but relatively little reaction from the biostatistical community. In this paper we argue that biostatisticians should be involved in preventing fraud (as well as unintentional errors), detecting it, and quantifying its impact on the outcome of clinical trials. We use the term &fraud' speci"cally to refer to data fabrication (making up data values) and falsi,cation (changing data values). Reported cases of such fraud involve cheating on inclusion criteria so that ineligible patients can enter the trial, and fabricating data so that no requested data are missing. Such types of fraud are partially preventable through a simpli"cation of the eligibility criteria and through a reduction in the amount of data requested. These two measures are feasible and desirable in a surprisingly large number of clinical trials, and neither of them in any way jeopardizes the validity of the trial results. With regards to detection of fraud, a brute force approach has traditionally been used, whereby the participating centres undergo extensive monitoring involving up to 100 per cent veri"cation of their case records. The cost-e!ectiveness of this approach seems highly debatable, since one could implement quality control through random sampling schemes, as is done in "elds other than clinical medicine. Moreover, there are statistical techniques available (but insu$ciently used) to detect &strange' patterns in the data including, but no limited to, techniques for studying outliers, inliers, overdispersion, underdispersion and correlations or lack thereof. These techniques all rest upon the premise that it is quite di$cult to invent plausible data, particularly highly dimensional multivariate data. The multicentric nature of clinical trials also o!ers an opportunity to check the plausibility of the data submitted by one centre by comparing them with the data from all other centres. Finally, with fraud detected, it is essential to quantify its likely impact upon the outcome of the clinical trial. Many instances of fraud in clinical trials, although morally reprehensible, have a negligible impact on the trial's scienti"c conclusions.


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