Literature DB >> 10611617

The role of biostatistics in the prevention, detection and treatment of fraud in clinical trials.

M Buyse1, S L George, S Evans, N L Geller, J Ranstam, B Scherrer, E Lesaffre, G Murray, L Edler, J Hutton, T Colton, P Lachenbruch, B L Verma.   

Abstract

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' specifically to refer to data fabrication (making up data values) and falsification (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 simplification 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 verification of their case records. The cost-effectiveness of this approach seems highly debatable, since one could implement quality control through random sampling schemes, as is done in fields other than clinical medicine. Moreover, there are statistical techniques available (but insufficiently 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 difficult to invent plausible data, particularly highly dimensional multivariate data. The multicentric nature of clinical trials also offers 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 scientific conclusions. Copyright 1999 John Wiley & Sons, Ltd.

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Year:  1999        PMID: 10611617     DOI: 10.1002/(sici)1097-0258(19991230)18:24<3435::aid-sim365>3.0.co;2-o

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  33 in total

1.  [From financial to scientific fraud : methods to detect discrepancies in the medical literature].

Authors:  G Schüpfer; J Hein; M Casutt; L Steiner; C Konrad
Journal:  Anaesthesist       Date:  2012-06       Impact factor: 1.041

2.  Fraud in clinical trials: complex problem, simple solutions?

Authors:  Junichi Sakamoto; Marc Buyse
Journal:  Int J Clin Oncol       Date:  2015-11-14       Impact factor: 3.402

3.  Statistical monitoring of data quality and consistency in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial.

Authors:  Catherine Timmermans; Erik Doffagne; David Venet; Lieven Desmet; Catherine Legrand; Tomasz Burzykowski; Marc Buyse
Journal:  Gastric Cancer       Date:  2015-08-23       Impact factor: 7.370

Review 4.  Research misconduct and data fraud in clinical trials: prevalence and causal factors.

Authors:  Stephen L George
Journal:  Int J Clin Oncol       Date:  2015-08-20       Impact factor: 3.402

Review 5.  Statistical challenges for central monitoring in clinical trials: a review.

Authors:  Koji Oba
Journal:  Int J Clin Oncol       Date:  2015-10-23       Impact factor: 3.402

6.  Data fraud in clinical trials.

Authors:  Stephen L George; Marc Buyse
Journal:  Clin Investig (Lond)       Date:  2015

7.  Are these data real? Statistical methods for the detection of data fabrication in clinical trials.

Authors:  Sanaa Al-Marzouki; Stephen Evans; Tom Marshall; Ian Roberts
Journal:  BMJ       Date:  2005-07-30

8.  Plagiarism - please don't copy.

Authors:  J K Aronson
Journal:  Br J Clin Pharmacol       Date:  2007-10       Impact factor: 4.335

Review 9.  Towards sustainable clinical trials.

Authors: 
Journal:  BMJ       Date:  2007-03-31

Review 10.  Reproducibility of research and preclinical validation: problems and solutions.

Authors:  Lajos Pusztai; Christos Hatzis; Fabrice Andre
Journal:  Nat Rev Clin Oncol       Date:  2013-10-01       Impact factor: 66.675

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