Fraud In Clinical Trials Research Articles

Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring

BackgroundA 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 MethodsThe 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.ResultsFive 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.ConclusionAn unsupervised approach to central monitoring, using mixed-effects statistical models, is effective at detecting centers with fraud or other data anomalies in clinical trials.

PNS334 SCIENTIFIC MISCONDUCT AND SYSTEMATIC LITERATURE REVIEWS (SLRS): THE IMPACT OF FRAUDULENT CLINICAL STUDIES ON EVIDENCE-BASED MEDICINE

The full extent of scientific misconduct including data fraud in clinical trials will, by its nature, never be known. In the highly regulated world of controlled clinical trials, misconduct is identified, and studies discounted from regulatory filings as a result. However, this does not necessarily correspond to the peer-reviewed literature. We briefly review the process for identifying and retracting fraudulent studies and these ease of access to such information. We used a case study of thromboprophylaxis after total knee arthroplasty (TKA) in which RECORD-4, was identified by the FDA as unreliable due to scientific misconduct and excluded from the FDA (and EMA) label. We demonstrate how inclusion of fraudulent studies can impact SLRs, and network meta-analysis (NMA). A series of recommendations to identify fraudulent studies are added to the systematic reviewer’s toolbox. A targeted review was conducted in PubMed to identify SLRs with meta-analyses or NMAs of RCTs including patients treated for thromboprophylaxis following TKA or total hip arthroplasty (THA). Peer-reviewed correspondence associated with RECORD-4 and FDA and EMA documentation was reviewed to determine how transparent the issues with RECORD-4 are in the public domain. Our review returned 38 references; data was extracted from 14 SLRs published between 2010-2019. All 14 SLRs included the RECORD-4 study, despite its unreliability being confirmed by the FDA in 2011. The primary RECORD-4 publication remains unretracted and associated correspondence to the pivotal publication does not mention any of the issues highlighted by the FDA. In early filings to the FDA, RECORD-4 is included whereas the final filing, and FDA and EMA labels do not mention RECORD-4. Improved transparency and communication are required to ensure studies found to involve scientific misconduct can be more easily recognised. In lieu of this more rigorous search burden is placed on the systematic reviewer.

Data fraud in clinical trials

Data fraud in clinical trials

Tracing the Weaknesses of the Regulatory Framework for Clinical Trial in Nigeria

Nigeria is one of the developing countries with a very weak and incomprehensive regulatory framework for clinical trials. Past abuses of human subjects in clinical trials and cases of negligence and outright fraud necessitate the need for a stronger legal framework. An outbreak of meningitis in Nigeria in 1996 led to clinical trial of Trovan Drugs by Pfizer, which left many of the research subjects seriously impaired. Some died while some suffered paralysis and brain damage. It was obvious Nigeria has a weak regulatory frame work from the incident that took place in the mentioned above at the Infectious Dieses Hospital (IDH) Kano. Available literatures have also shown that at the time of the trial in Kano, there was no ethics committee to ensure ethics review process and monitor the ongoing trial. Objectives: This research aimed at examining the Nigerian regulatory framework to bring out its weaknesses. It will also examine the strength and weakness of the current regulatory agencies, and suggest better regulatory framework that will make Nigerians less vulnerable to manipulation and fraud in clinical trials. Methodology: Library research is used to study both primary and secondary legal resources. Finding: Lack of standard regulatory body and lack unified legal framework is the cause of failure to protect human subject in Nigeria.

Fraud in clinical trials: complex problem, simple solutions?

[Sakamoto, Junichi] Tokai Cent Hosp, 4 Chome 6-2 Sohara Higashijimacho, Kakamigahara, Gifu 5048601, Japan. [Buyse, Marc] Int Inst Drug Dev, Cambridge, MA USA. [Buyse, Marc] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat I BioSt, Hasselt, Belgium.

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

The disclosure of cases of research misconduct in clinical trials, conventionally defined as fabrication, falsification or plagiarism, has been a disturbingly common phenomenon in recent years. Such cases can potentially harm patients enrolled on the trials in question or patients treated based on the results of those trials and can seriously undermine the scientific and public trust in the validity of clinical trial results. Here, I review what is known about the prevalence of research misconduct in general and the contributing or causal factors leading to the misconduct. The evidence on prevalence is unreliable and fraught with definitional problems and with study design issues. Nevertheless, the evidence taken as a whole seems to suggest that cases of the most serious types of misconduct, fabrication and falsification (i.e., data fraud), are relatively rare but that other types of questionable research practices are quite common. There have been many individual, institutional and scientific factors proposed for misconduct but, as is the case with estimates of prevalence, reliable empirical evidence on the strength and relative importance of these factors is lacking. However, it seems clear that the view of misconduct as being simply the result of aberrant or self-delusional personalities likely underestimates the effect of other important factors and inhibits the development of effective prevention strategies.

Strategies for dealing with fraud in clinical trials.

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.

Data fraud in clinical trials.

Highly publicized cases of fabrication or falsification of data in clinical trials have occurred in recent years and it is likely that there are additional undetected or unreported cases. We review the available evidence on the incidence of data fraud in clinical trials, describe several prominent cases, present information on motivation and contributing factors and discuss cost-effective ways of early detection of data fraud as part of routine central statistical monitoring of data quality. Adoption of these clinical trial monitoring procedures can identify potential data fraud not detected by conventional on-site monitoring and can improve overall data quality.

Central statistical monitoring: Detecting fraud in clinical trials

Central statistical monitoring in multicenter trials could allow trialists to identify centers with problematic data or conduct and intervene while the trial is still ongoing. Currently, there are few published models that can be used for this purpose. To develop and validate a series of risk scores to identify fabricated data within a multicenter trial, to be used in central statistical monitoring. We used a database from a multicenter trial in which data from 9 of 109 centers were documented to be fabricated. These data were used to build a series of risk scores to predict fraud at centers. All analyses were performed at the level of the center. Exploratory factor analysis was used to select from 52 possible predictors, chosen from a variety of previously published methods. The final models were selected from a total of 18 independent predictors, based on the factors identified. These models were converted to risk scores for each center. Five different risk scores were identified, and each had the ability to discriminate well between centers with and without fabricated data (area under the curve values ranged from 0.90 to 0.95). True- and false-positive rates are presented for each risk score to arrive at a recommended cutoff of seven or above (high risk score). We validated these risk scores, using an independent multicenter trial database that contained no data fabrication and found the occurrence of false-positive high scores to be low and comparable to the model-building data set. These risk score have been validated only for their false-positive rate and require validation within another trial that contains centers that have fabricated data. Validation in noncardiovascular trials is also required to gage the usefulness of these risk scores in central statistical monitoring. With further validation, these risk scores could become part of a series of tools that provide evidence-based central statistical monitoring, which in turn can improve the efficiency of trials, and minimize the need for more expensive on-site monitoring.

Fraud in Clinical Trials

Abstract Fraud is surprisingly hard to conceal. If you make up or alter the data in a clinical trial you will leave a trail that a good statistician can follow. Christopher Weir and Gordon Murray tell would-be detectives where the clues will be.

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

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

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

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.

Perspectives on Scientific Misconduct and Fraud in Clinical Trials

(1997). Perspectives on Scientific Misconduct and Fraud in Clinical Trials. CHANCE: Vol. 10, No. 4, pp. 3-5.