Assessing the performance of methods for central statistical monitoring of a binary or continuous outcome in multi-center trials: A simulation study

BACKGROUND

Quality study monitoring is fundamental to patient safety and data integrity. Regulators and industry consortia have increasingly advocated for risk-based monitoring (RBM) and central statistical monitoring (CSM) for more effective and efficient monitoring. Assessing which statistical methods underpin these approaches can best identify unusual data patterns in multi-center clinical trials that may be driven by potential systematic errors is important.

METHODS

We assessed various CSM techniques, including cross-tests, fixed-effects, mixed-effects, and finite mixture models, across scenarios with different sample sizes, contamination rates, and overdispersion via simulation. Our evaluation utilized threshold-independent metrics such as the area under the curve (AUC) and average precision (AP), offering a fuller picture of CSM performance.

RESULTS

All CSM methods showed consistent characteristics across center sizes or overdispersion. The adaptive finite mixture model outperformed others in AUC and AP, especially at 30% contamination, upholding high specificity unless converging to a single-component model due to low contamination or deviation. The mixed-effects model performed well at lower contamination rates. However, it became conservative in specificity and exhibited declined performance for binary outcomes under high deviation. Cross-tests and fixed-effects methods underperformed, especially when deviation increased.

CONCLUSION

Our evaluation explored the merits and drawbacks of multiple CSM methods, and found that relying on sensitivity and specificity alone is likely insufficient to fully measure predictive performance. The finite mixture method demonstrated more consistent performance across scenarios by mitigating the influence of outliers. In practice, considering the study-specific costs of false positives/negatives with available resources for monitoring is important.

Does Central Statistical Monitoring Improve Data Quality? An Analysis of 1,111 Sites in 159 Clinical Trials

BACKGROUND

Central monitoring aims at improving the quality of clinical research by pro-actively identifying risks and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. This paper, focusing on statistical data monitoring (SDM), is the second of a series that attempts to quantify the impact of central monitoring in clinical trials.

MATERIAL AND METHODS

Quality improvement was assessed in studies using SDM from a single large central monitoring platform. The analysis focused on a total of 1111 sites that were identified as at-risk by the SDM tests and for which the study teams conducted a follow-up investigation. These sites were taken from 159 studies conducted by 23 different clinical development organizations (including both sponsor companies and contract research organizations). Two quality improvement metrics were assessed for each selected site, one based on a site data inconsistency score (DIS, overall -log10 P-value of the site compared with all other sites) and the other based on the observed metric value associated with each risk signal.

RESULTS

The SDM quality metrics showed improvement in 83% (95% CI, 80-85%) of the sites across therapeutic areas and study phases (primarily phases 2 and 3). In contrast, only 56% (95% CI, 41-70%) of sites showed improvement in 2 historical studies that did not use SDM during study conduct.

CONCLUSION

The results of this analysis provide clear quantitative evidence supporting the hypothesis that the use of SDM in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites.

Application of Statistical Methods for Central Statistical Monitoring and Implementations on the German Multiple Sclerosis Registry

Monitoring of clinical trials is a fundamental process required by regulatory agencies. It assures the compliance of a center to the required regulations and the trial protocol. Traditionally, monitoring teams relied on extensive on-site visits and source data verification. However, this is costly, and the outcome is limited. Thus, central statistical monitoring (CSM) is an additional approach recently embraced by the International Council for Harmonisation (ICH) to detect problematic or erroneous data by using visualizations and statistical control measures. Existing implementations have been primarily focused on detecting inlier and outlier data. Other approaches include principal component analysis and distribution of the data. Here we focus on the utilization of comparisons of centers to the Grand mean for different model types and assumptions for common data types, such as binomial, ordinal, and continuous response variables. We implement the usage of multiple comparisons of single centers to the Grand mean of all centers. This approach is also available for various non-normal data types that are abundant in clinical trials. Further, using confidence intervals, an assessment of equivalence to the Grand mean can be applied. In a Monte Carlo simulation study, the applied statistical approaches have been investigated for their ability to control type I error and the assessment of their respective power for balanced and unbalanced designs which are common in registry data and clinical trials. Data from the German Multiple Sclerosis Registry (GMSR) including proportions of missing data, adverse events and disease severity scores were used to verify the results on Real-World-Data (RWD).

Performance of four centralized statistical monitoring methods for early detection of an atypical center in a multicenter study

Background

Ensuring the quality of data is essential for the credibility of a multicenter clinical trial. Centralized Statistical Monitoring (CSM) of data allows the detection of a center in which the distribution of a specific variable is atypical compared to other centers. The ideal CSM method should allow early detection of problem and therefore involve the fewest possible participants.

Methods

We simulated clinical trials and compared the performance of four CSM methods (Student, Hatayama, Desmet, Distance) to detect whether the distribution of a quantitative variable was atypical in one center in relation to the others, with different numbers of participants and different mean deviation amplitudes.

Results

The Student and Hatayama methods had good sensitivity but poor specificity, which disqualifies them for practical use in CSM. The Desmet and Distance methods had very high specificity for detecting all the mean deviations tested (including small values) but low sensitivity with mean deviations less than 50%.

Conclusion

Although the Student and Hatayama methods are more sensitive, their low specificity would lead to too many alerts being triggered, which would result in additional unnecessary control work to ensure data quality. The Desmet and Distance methods have low sensitivity when the deviation from the mean is low, suggesting that the CSM should be used alongside other conventional monitoring procedures rather than replacing them. However, they have excellent specificity, which suggests they can be applied routinely, since using them takes up no time at central level and does not cause any unnecessary workload in investigating centers.

Does Central Monitoring Lead to Higher Quality? An Analysis of Key Risk Indicator Outcomes

BACKGROUND

Central monitoring, which typically includes the use of key risk indicators (KRIs), aims at improving the quality of clinical research by pro-actively identifying and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. However, there has to-date been a relative lack of direct quantitative evidence published supporting the claim that central monitoring actually leads to improved quality.

MATERIAL AND METHODS

Nine commonly used KRIs were analyzed for evidence of quality improvement using data retrieved from a large central monitoring platform. A total of 212 studies comprising 1676 sites with KRI signals were used in the analysis, representing central monitoring activity from 23 different sponsor organizations. Two quality improvement metrics were assessed for each KRI, one based on a statistical score (p-value) and the other based on a KRI's observed value.

RESULTS

Both KRI quality metrics showed improvement in a vast majority of sites (82.9% for statistical score, 81.1% for observed KRI value). Additionally, the statistical score and the observed KRI values improved, respectively by 66.1% and 72.4% on average towards the study average for those sites showing improvement.

CONCLUSION

The results of this analysis provide clear quantitative evidence supporting the hypothesis that use of KRIs in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites.

Machine learning for detecting centre-level irregularities in randomized controlled trials: A pilot study

Centralized statistical monitoring is sometimes employed as an alternative to onsite monitoring for randomized control trials. Current central monitoring methods have limitations, in that they are relatively resource intensive and do not necessarily generalize to studies where an irregularity pattern has not been observed before. Machine learning has been effective in detecting irregularities in industries such as finance and manufacturing, but to date none have been applied to clinical trials. We conducted a pilot study for the use of machine learning to identify center-level irregularities in data from multicenter clinical trials. We employed unsupervised machine learning methods, which do not rely on labelled data, and therefore allow for the automated discovery of previously unseen irregularity patterns while maintaining flexibility when applied to new data with different structures. This pilot study employs unsupervised machine learning to compute distance matrices between centres, which we used to produce centre-level continuous features. We then used a one-class support vector machine to learn the underlying distribution of each data set to identify data that was substantially different from these distributions. We evaluated our approach against current automatable centralized monitoring methods on two trials with known irregularities. While current approaches performed well on one trial (AUROC 0.752 for monitoring vs. 0.584 for machine learning), our techniques performed substantially better on the other (AUROC 0.140 for monitoring vs 0.728 for machine learning). The results of this pilot study suggest both the feasibility and the potential value of a machine learning-based approach to irregularity detection in RCTs.

Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring

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.

Dynamic methods for ongoing assessment of site-level risk in risk-based monitoring of clinical trials: A scoping review

Background/Aims

It is increasingly recognised that reliance on frequent site visits for monitoring clinical trials is inefficient. Regulators and trialists have recently encouraged more risk-based monitoring. Risk assessment should take place before a trial begins to define the overarching monitoring strategy. It can also be done on an ongoing basis, 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 the International Conference on Harmonization Good Clinical Practice guidance, ‘targeted on-site monitoring’. We conducted a scoping review to identify such methods, to establish if any were supported by adequate evidence to allow wider implementation, and to guide future developments in this field of research.

Methods

We used seven publication databases, two sets of methodological conference abstracts and an Internet search engine to identify 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 or not directly relevant to our research question. We used reference and citation searches to find additional relevant reports. We extracted data using a predefined template. We contacted authors to request additional information about included reports.

Results

We included 30 reports in our final dataset, of which 21 were peer-reviewed publications. In all, 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, typically exploring the methods’ characteristics using real trial data without known integrity issues. Of the 21 with some effectiveness assessment, most contained limited information about whether or not concerns identified through central monitoring constituted meaningful problems. Several reports demonstrated good classification ability based on more than one classification statistic, but never without caveats of unclear reporting or other classification statistics being low or unavailable. 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.

Conclusion

Our 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. Despite some promising evidence and some self-justifying benefits for data cleaning activity, many proposed methods 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.

Clinical trial preparations for the next pandemic

This paper describes the need to prepare for the development of antiviral therapeutics for the next pandemic. Preparation would consist of a stockpiling of best practices for clinical trial design, analysis and operations during the current SARS-CoV-2 pandemic as well as continuous development of treatments and methodology between pandemics. This development would be facilitated by a global clinical trial pandemic reserve similar to the military reserves consisting of medical and quantitative methods professionals who would remain engaged between pandemics. Continuous identification of potential antiviral drugs and diagnostic methods would also be needed. Specific methodology addressed includes the importance of large simple trials, follow up time, efficacy endpoint, appropriate estimands, non-inferiority trials, more sophisticated patient accrual models and procedures for data sharing between clinical trials.

Design and conduct of confirmatory chronic pain clinical trials

The purpose of this article is to provide readers with a basis for understanding the emerging science of clinical trials and to provide a set of practical, evidence-based suggestions for designing and executing confirmatory clinical trials in a manner that minimizes measurement error. The most important step in creating a mindset of quality clinical research is to abandon the antiquated concept that clinical trials are a method for capturing data from clinical practice and shifting to a concept of the clinical trial as a measurement system, consisting of an interconnected set of processes, each of which must be in calibration for the trial to generate an accurate and reliable estimate of the efficacy (and safety) of a given treatment. The status quo of inaccurate, unreliable, and protracted clinical trials is unacceptable and unsustainable. This article gathers aspects of study design and conduct under a single broad umbrella of techniques available to improve the accuracy and reliability of confirmatory clinical trials across traditional domain boundaries.

Central statistical monitoring of investigator-led clinical trials in oncology

Investigator-led clinical trials are pragmatic trials that aim to investigate the benefits and harms of treatments in routine clinical practice. These much-needed trials represent the majority of all trials currently conducted. They are however threatened by the rising costs of clinical research, which are in part due to extensive trial monitoring processes that focus on unimportant details. Risk-based quality management focuses, instead, on “things that really matter”. We discuss the role of central statistical monitoring as part of risk-based quality management. We describe the principles of central statistical monitoring, provide examples of its use, and argue that it could help drive down the cost of randomized clinical trials, especially investigator-led trials, whilst improving their quality.

Bayesian central statistical monitoring using finite mixture models in multicenter clinical trials

Background

Central monitoring (CM), in which data across all clinical sites are monitored, has an important role in risk-based monitoring. Several statistical methods have been proposed to compare patient outcomes among the sites for detecting atypical sites that have different trends in observed data. These methods assume that the number of clinical sites is not small, e.g., 100 or more. In addition, the proportion of atypical sites is assumed to be relatively small. However, in actuality, the central statistical monitoring (CSM) has to be implemented in small or moderate sized clinical trials such as small phase II clinical trials. The number of sites is no longer large in such situations. Therefore, it is of concern that existing methods may not work efficiently in CM of small or moderate sized clinical trials. In the light of this problem, we propose a Bayesian CSM method to detect atypical sites as the robust method against the existence of atypical sites.

Methods

We use Bayesian finite mixture models (FMM) to model patient outcome values of both atypical and typical sites. In the method, the distributions of outcome values in normal sites are determined by choosing the body distribution, which has the largest mixture parameter value of finite mixture models based on the assumption that normal sites are in the majority. Atypical sites are detected by the criterion based on the posterior predictive distribution of normal site's outcome values derived from only the chosen body distribution.

Results

Proposed method is evaluated by cumulative detection probability and type I error averaged over sites every round of CSM under the various scenarios, being compared with the conventional type analysis. If the total number of patients enrolled is 48, the proposed method is superior at least 10% for any shift sizes at the 2nd and the 3rd rounds. If the total number of patients is 96, both methods show similar detection probability for only one atypical site and large shift size. However, the proposed method is superior for the other scenarios. It is observed that all the type I errors averaged over sites are little difference between the methods at all the scenarios.

Conclusion

We propose a Bayesian CSM method which works efficiently in a practical use of CM. It is shown that our method detects atypical sites with high probability regardless of the proportion of the atypical sites under the small clinical trial settings which is the target of our proposed method.

Improving Study Conduct and Data Quality in Clinical Trials of Chronic Pain Treatments: IMMPACT Recommendations

The estimated probability of progressing from phase 3 analgesic clinical trials to regulatory approval is approximately 57%, suggesting that a considerable number of treatments with phase 2 trial results deemed sufficiently successful to progress to phase 3 do not yield positive phase 3 results. Deficiencies in the quality of clinical trial conduct could account for some of this failure. An Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials meeting was convened to identify potential areas for improvement in trial conduct in order to improve assay sensitivity (ie, ability of trials to detect a true treatment effect). We present recommendations based on presentations and discussions at the meeting, literature reviews, and iterative revisions of this article. The recommendations relate to the following areas: 1) study design (ie, to promote feasibility), 2) site selection and staff training, 3) participant selection and training, 4) treatment adherence, 5) data collection, and 6) data and study monitoring. Implementation of these recommendations may improve the quality of clinical trial data and thus the validity and assay sensitivity of clinical trials. Future research regarding the effects of these strategies will help identify the most efficient use of resources for conducting high quality clinical trials. PERSPECTIVE: Every effort should be made to optimize the quality of clinical trial data. This manuscript discusses considerations to improve conduct of pain clinical trials based on research in multiple medical fields and the expert consensus of pain researchers and stakeholders from academia, regulatory agencies, and industry.

Detection of atypical data in multicenter clinical trials using unsupervised statistical monitoring

BACKGROUND/AIMS

A risk-based approach to clinical research may include a central statistical assessment of data quality. We investigated the operating characteristics of unsupervised statistical monitoring aimed at detecting atypical data in multicenter experiments. The approach is premised on the assumption that, save for random fluctuations and natural variations, data coming from all centers should be comparable and statistically consistent. Unsupervised statistical monitoring consists of performing as many statistical tests as possible on all trial data, in order to detect centers whose data are inconsistent with data from other centers.

METHODS

We conducted simulations using data from a large multicenter trial conducted in Japan for patients with advanced gastric cancer. The actual trial data were contaminated in computer simulations for varying percentages of centers, percentages of patients modified within each center and numbers and types of modified variables. The unsupervised statistical monitoring software was run by a blinded team on the contaminated data sets, with the purpose of detecting the centers with contaminated data. The operating characteristics (sensitivity, specificity and Youden's J-index) were calculated for three detection methods: one using the p-values of individual statistical tests after adjustment for multiplicity, one using a summary of all p-values for a given center, called the Data Inconsistency Score, and one using both of these methods.

RESULTS

The operating characteristics of the three methods were satisfactory in situations of data contamination likely to occur in practice, specifically when a single or a few centers were contaminated. As expected, the sensitivity increased for increasing proportions of patients and increasing numbers of variables contaminated. The three methods showed a specificity better than 93% in all scenarios of contamination. The method based on the Data Inconsistency Score and individual p-values adjusted for multiplicity generally had slightly higher sensitivity at the expense of a slightly lower specificity.

CONCLUSIONS

The use of brute force (a computer-intensive approach that generates large numbers of statistical tests) is an effective way to check data quality in multicenter clinical trials. It can provide a cost-effective complement to other data-management and monitoring techniques.

Effects of an exercise program on hepatic metabolism, hepatic fat, and cardiovascular health in overweight/obese adolescents from Bogotá, Colombia (the HEPAFIT study): study protocol for a randomized controlled trial

Background

A considerable proportion of contemporary youth have a high risk of obesity-related disorders such as cardiovascular disease, metabolic syndrome, or non-alcoholic fatty liver disease (NAFLD). Although there is consistent evidence for the positive effects of physical activity on several health aspects, most adolescents in Colombia are sedentary. It is, therefore, important to implement strategies that generate changes in lifestyle. The HEPAFIT study aims to examine whether a 6-month exercise program has benefits for hepatic fat content and cardiovascular health outcomes among overweight/obese adolescents from Bogotá, Colombia.

Methods/design

Altogether, 100 hundred overweight/obese, sedentary adolescents (aged 11–17 years) attending two public schools in Bogotá, Colombia, will be included in a parallel-group randomized controlled trial. Adolescents will be randomly assigned to an intervention group following one of four curricula: (1) the standard physical education curriculum (60 min per week of physical activity, n = 25) at low-to-moderate intensity; (2) a high-intensity physical education curriculum (HIPE, n = 25), consisting of endurance and resistance games and non-competitive activities, such as running, gymkhanas, lifting, pushing, wrestling, or hauling, for 60-min sessions, three times per week, with an energy expenditure goal of 300 to 500 kcal/session at 75–85% maximum heart rate (HRmax); (3) a low-to-moderate intensity physical education curriculum (LIPE, n = 25) consisting of endurance and resistance games and non-competitive activities (e.g., chasing, sprinting, dribbling, or hopping) for 60-min sessions, three times per week with an energy expenditure goal of 300 kcal/session at 55–75% HRmax; and (4) a combined HIPE and LIPE curriculum (n = 25). The HIPE, LIPE, and combined interventions were performed in addition to the standard physical education curriculum. The primary outcome for effectiveness is liver fat content, as measured by the controlled attenuation parameter 1 week after the end of the intervention program.

Discussion

The translational focus may be suitable for collecting new information in a school setting on the possible effects of physical activity interventions to reduce liver fat content and to improve metabolic profiles and the cardiometabolic health of overweight/obese adolescents. This may lead to the more efficient use of school physical education resources.

Trial registration

ClinicalTrials.gov, NCT02753231. Registered on 21 April 2016.

Electronic supplementary material

The online version of this article (10.1186/s13063-018-2721-5) contains supplementary material, which is available to authorized users.

Strategic Shift of Statistical Review on Data Quality Assessment for New Drug Applications in China

Data quality is critical for clinical trials to obtain robust conclusions about drug safety and efficacy evaluation. Effective data quality evaluation has been one of the major obstacles to new drug approvals in China, which hinders innovation in drug discovery and development ultimately. To improve the data quality submitted for regulatory drug approval, the China Food and Drug Administration (CFDA) has issued serial official announcements and industry guidelines regarding improvement of the clinical trial data integrity and quality since 2015. These announcements and follow-up measures are shaping up the entire pharmaceutical industry in China. While data quality is being strongly emphasized more than ever at the trial conduction phase, it is still an open question about how to assess data quality effectively at the review stage. Thus, this article describes the authors' standpoints to assess the quality and integrity of submitted clinical data via statistical review methods including advanced risk-based approaches, which may bring significant impact to new drug applications and motivate sustainable development of innovative medicines in China.

Multidimensional reduction of multicentric cohort heterogeneity: An alternative method to increase statistical power and robustness

Modern clinical research takes advantage of multicentric cohorts to increase sample size and gain in statistical power. However, combining individuals from different recruitment centers provides heterogeneity in the dataset that needs to be accounted for to obtain robust results. Sophisticated statistical multivariate models adjusting for center effect can be implemented, but they can become unstable and can be complex to interpret with the increasing number of covariates to consider. Here, we present a multidimensional reduction technique to identify heterogeneity in a French multicentric cohort of hematopoietic stem cell transplantations and characterize a homogeneous subgroup prior to performing simple statistical univariate analyses. The exclusion of outliers allowed the identification of two genetic factors associated with post-transplantation overall survival. We therefore provide proof-of-concept that a sample size reduction method can efficiently account for heterogeneity and center effect in multicentric cohorts while increasing statistical power and robustness for discovery of new association signals.

The impact of clinical trial monitoring approaches on data integrity and cost--a review of current literature

PURPOSE

Monitoring is a costly requirement when conducting clinical trials. New regulatory guidance encourages the industry to consider alternative monitoring methods to the traditional 100 % source data verification (SDV) approach. The purpose of this literature review is to provide an overview of publications on different monitoring methods and their impact on subject safety data, data integrity, and monitoring cost.

METHODS

The literature search was performed by keyword searches in MEDLINE and hand search of key journals. All publications were reviewed for details on how a monitoring approach impacted subject safety data, data integrity, or monitoring costs.

RESULTS

Twenty-two publications were identified. Three publications showed that SDV has some value for detection of not initially reported adverse events and centralized statistical monitoring (CSM) captures atypical trends. Fourteen publications showed little objective evidence of improved data integrity with traditional monitoring such as 100 % SDV and sponsor queries as compared to reduced SDV, CSM, and remote monitoring. Eight publications proposed a potential for significant cost reductions of monitoring by reducing SDV without compromising the validity of the trial results.

CONCLUSIONS

One hundred percent SDV is not a rational method of ensuring data integrity and subject safety based on the high cost, and this literature review indicates that reduced SDV is a viable monitoring method. Alternative methods of monitoring such as centralized monitoring utilizing statistical tests are promising alternatives but have limitations as stand-alone tools. Reduced SDV combined with a centralized, risk-based approach may be the ideal solution to reduce monitoring costs while improving essential data quality.

Data-driven risk identification in phase III clinical trials using central statistical monitoring

Our interest lies in quality control for clinical trials, in the context of risk-based monitoring (RBM). We specifically study the use of central statistical monitoring (CSM) to support RBM. Under an RBM paradigm, we claim that CSM has a key role to play in identifying the "risks to the most critical data elements and processes" that will drive targeted oversight. In order to support this claim, we first see how to characterize the risks that may affect clinical trials. We then discuss how CSM can be understood as a tool for providing a set of data-driven key risk indicators (KRIs), which help to organize adaptive targeted monitoring. Several case studies are provided where issues in a clinical trial have been identified thanks to targeted investigation after the identification of a risk using CSM. Using CSM to build data-driven KRIs helps to identify different kinds of issues in clinical trials. This ability is directly linked with the exhaustiveness of the CSM approach and its flexibility in the definition of the risks that are searched for when identifying the KRIs. In practice, a CSM assessment of the clinical database seems essential to ensure data quality. The atypical data patterns found in some centers and variables are seen as KRIs under a RBM approach. Targeted monitoring or data management queries can be used to confirm whether the KRIs point to an actual issue or not.

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.