Data fraud in clinical trials

George, Stephen L.; Buyse, Marc

doi:10.4155/cli.14.116

Cited by 103 publications

(92 citation statements)

References 61 publications

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“…30 Apparently, the true proportion is difficult to estimate due to a composite of complex different reasons. As one might expect, in any attempt of estimation via survey of investigators, researchers who deliberately commit fraud are not liable to be anticipated about having defiled information.…”

Section: The Incidence Of Data Adulterationmentioning

confidence: 99%

Qualitative features in clinical trials: coordinates for prevention of passive and active misconduct

et al. 2018

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For many years, the quality concept in clinical trials has been discussed and recommended by Good Clinical Practice (GCP) guidelines. Regulatory Authorities and also the Public Involvement anticipate that the pharmaceutical industry will concentrate on creating quality frameworks amid the arranging and leading of conventions of controlled protocols. Nevertheless, many factors have been suggested as contributing to the occurrence of scientific misconduct within the research field, such as: personal and financial interests, site monitoring, available resources, workload, competition among investigators, and the implicit consent of sponsors. The negligence on data fraud represents not only omission but misconduct as well, in this case, a passive attitude intrinsically related to the act of transgression. A properly culture of research must be based on a fundamental ethos of integrity, openness and honest work of high quality in all parts of the research process. There is a need to change the focus from inspection-based quality improvement to planned systematic quality management within clinical trials. In search for a monitoring improvement, a full statistical way to deal with information recognition comprises of executing however many measurable tests as could be allowed on whatever number clinical information factors as could be expected under varied circumstances. Adoption of specific and preventive clinical trial monitoring procedures can identify potential misconduct and data fraud leading to improvement in overall data quality and scientific reports.

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Section: The Incidence Of Data Adulterationmentioning

confidence: 99%

Qualitative features in clinical trials: coordinates for prevention of passive and active misconduct

et al. 2018

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“…For example, earlier research highlighted that researchers are often quite bad in fabricating data that look genuinely stochastic (Mosimann et al 2002, Mosimann et al 1995. Moreover, if a randomized experimental design includes an honest mistake, for instance assigning all females to the control group, stochasticity does not fully apply anymore.…”

Section: Methodsologymentioning

confidence: 99%

“…For clinical trials conducted across different locations, central statistical monitoring can be applied to detect aberrant data by comparing the summary results from different collection sites (Timmermans et al 2015). Such a monitoring tool is useful because fully verifying the raw data for all locations is practically infeasible .…”

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confidence: 99%

Problematic trial detection in ClinicalTrials.gov

Hartgerink¹,

George²

2015

RIO

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Executive summaryClinical trials are crucial in determining the effectiveness of treatments and directly affect clinical and policy decisions. These decisions are undermined if the data are problematic due to data fabrication or other errors. Researchers have worked on developing statistical methods to detect problematic data. This project aims to develop new methods and apply them to results reported in the ClinicalTrials.gov database. Using both established and and the newly developed statistical methods we will investigate the prevalence of problematic data, trends of problematic data over time, and whether the prevalence of problematic data is predicted by trial characteristics such as funding type.

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“…The issue of fraud in clinical trials comes under the general heading of research misconduct, which is defined as "fabrication, falsification or plagiarism in proposing, performing or reviewing research or in reporting research results" [1]. There are increasing reports of research misconduct in clinical trials, and the practice has even been the subject of popular fiction [2].…”

Section: Introductionmentioning

confidence: 99%

Strategies for dealing with fraud in clinical trials

Herson

2015

Int J Clin Oncol

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Research misconduct and fraud in clinical research is an increasing problem facing the scientific community. This problem is expected to increase due to discoveries in central statistical monitoring and with the increase in first-time clinical trial investigators in the increasingly global reach of oncology clinical trials. This paper explores the most common forms of fraud in clinical trials in order to develop offensive and defensive strategies to deal with fraud. The offensive strategies are used when fraud is detected during a trial and the defensive strategies are those design strategies that seek to minimize or eliminate the effect of fraud. This leads to a proposed fraud recovery plan (FRP) that would be specified before the start of a clinical trial and would indicate actions to be taken upon detecting fraud of different types. Statistical/regulatory issues related to fraud include: dropping all patients from a site that committed fraud, or just the fraudulent data (perhaps replacing the latter through imputation); the role of intent-to-treat analysis; effect on a planned interim analysis; effect on stratified analyses and model adjustment when fraud is detected in covariates; effect on trial-wide randomization, etc. The details of a typical defensive strategy are also presented. It is concluded that it is best to follow a defensive strategy and to have an FRP in place to follow if fraud is detected during the trial.

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Data fraud in clinical trials

Cited by 103 publications

References 61 publications

Qualitative features in clinical trials: coordinates for prevention of passive and active misconduct

Qualitative features in clinical trials: coordinates for prevention of passive and active misconduct

Problematic trial detection in ClinicalTrials.gov

Strategies for dealing with fraud in clinical trials

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