Risk-Based Monitoring Software

Webinar Recording: What Does ICH E6 R2 Mean for Me and My Company?

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This week, CluePoints hosted a webinar with Paragon Solutions, which was geared towards preparing attendees on how to take advantage of the ICH E6 revisions to ensure appropriate consideration of risk in study design and management. Attendees learned about the key ICH E6 updates related to Risk-Based Monitoring and their purpose, how Central Statistical Monitoring and Key Risk Indicators will work for them, and how to use the E6 R2 to support their organization’s transition to Risk-Based Monitoring.

If you missed the webinar, you can access the on-demand recording here. Please feel free to share the URL with your colleagues.

We hope to see you at future CluePoints webinars!

6th Annual CROWN Congress: Sessions you Can’t Afford to Miss + 15% Discount Code

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It’s really no secret that the evolving regulatory landscape for clinical trials is a hot topic amongst the community. Just take a look at the agenda for the 6th Annual CROWN Congress, it’s jam-packed with sessions focused on the eagerly anticipated ICH E6 revisions, the new EU 536/2014 clinical trial regulations, risk management and centralized monitoring.  Of course, with the increased pressures coming from the regulatory bodies, sponsors are racing to implement recommendations to ensure that their clinical trials run in line with guidance.

With that in mind, we’ve put together a list of the #CROWNClinical ’17 sessions that you absolutely can’t afford to miss:

  • Risk-Based Monitoring and the Centralized Monitoring Model – Mapping out and Executing a Centralized Monitoring Approach to Optimize Operational Quality and Efficiency

    Speaker: Steve Young, Senior VP of US Operations, CluePoints
    Date & Time: Wednesday, March 8, 2017, at 3:30pm

  • Changing Regulations – Evaluating the Changing Global Regulatory Environment and the Impact on Clinical Risk Management

    Speaker: Doreen McGirl, North America Lead, Global Quality Business Operations, Otsuka
    Date & Time: Thursday, March 9, 2017, at 08:30am

  • Proactive Risk Management – Proactively Planning for Risk Realization and Managing Response

    Speaker: Stephen Potter, Director, Clinical Development Quality, Pfizer
    Date & Time: Thursday, March 9, 2017, at 09:15am

  • Clinical Operational Shifts – Examining how Evolving Regulations, Quality Expectations and Risk Assessments are Driving Changes in Clinical Operations

    Speaker: Federico A. Feldstein, J.D., Vice President, Global Head of Bio Research Quality and Compliance, The Janssen Pharmaceutical Companies of Johnson & Johnson
    Date & Time: Thursday, March 9, 2017, at 1:00pm

Not registered yet? – use discount code ‘C846CLUEPOINTS’ to save 15% on registration.

If you would like to schedule some time to discuss the upcoming regulatory revisions, Risk-Based Monitoring implementation, or anything else, please send an email to

See you there!

FDA Signs Agreement with CluePoints to Explore a Data-Driven Approach to Quality Oversight in Clinical Trials

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12 October 2016

Cambridge, MA –  FDA and its stakeholders have an interest in assuring the integrity of clinical trial data and the protection of participants during the conduct of clinical research.  Misconduct in clinical research, including, but not limited to the falsification or omission of data in reporting research results, places all subjects in that trial at possible safety risk.  Fraud jeopardizes the reliability of data submitted to FDA, and undermines the Agency’s mission to protect and promote public health. FDA and other regulators rely on whistleblowers and site inspections to detect signs of possible misconduct.

Due to the volume of product submissions, FDA can only inspect a small proportion of clinical trial sites.  The determination of which sites to inspect can involve recommendations by clinical and statistical reviewers, CDER’s risk based site selection tool and FDA inspectors’ judgment and experiences.

This Cooperative Research and Development Agreement (CRADA) explores a data driven approach to selecting sites which exhibit data anomalies indicative of fraud, misconduct or sloppiness.  Under this CRADA, FDA and CluePoints, Inc. will develop and test enhancements to CluePoints existing software to produce an ordered list of “anomalous sites”, i.e. sites whose data are highly inconsistent with data from other sites; explore “moderators of treatment effect”, i.e. factors such as center, region, or country that have a statistically significant impact on the magnitude of treatment effect; add statistical tests and models to those already in the existing software; refine the scoring system used to identify outlying centers; add an exploratory tool to identify moderators of treatment effect; test and implement the software in a high performance computing environment; and develop a user-friendly interface for use by medical reviewers and other interested parties at FDA.

Anticipated benefits to the FDA of the CRADA’s data driven approach include the detection of anomalous sites which may have escaped detection previously, rapid turnaround of results, the ability to determine the nature and extent of data anomalies, and the ability to explore the interaction of various factors with data quality.  These benefits are expected to not only accrue to the site inspection process and improve data quality for all reviewers, but may also inform the efforts of clinical and statistical reviewers to conduct sensitivity analyses, subgroup analyses and site by treatment effect explorations.

For further information on CluePoints, please visit

About CluePoints

CluePoints® is a Central Statistical Monitoring solution that has been designed and perfected over the last 10 years. It employs unique statistical algorithms to determine the quality, accuracy, and integrity of clinical trial data both during and after study conduct. Aligned with guidance from the FDA and EMA, CluePoints® is deployed to support traditional on-site monitoring and to drive a risk-based monitoring strategy. The value of using CluePoints® lies in its powerful and timely ability to identify anomalous data and site errors allowing improvement in clinical data quality, optimization of on-site monitoring and a significant reduction in overall regulatory submission risk.

Media Contact

Patrick Hughes – Chief Commercial Officer, CluePoints
+44 (0) 7703 532 749

Central Statistical Monitoring Should Support Key Risk Indicators

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As you may well know, much of the early work in Risk-Based Monitoring has focused on relatively simple Key Risk Indicators (KRIs) and traffic-light dashboards, which are easy to understand. However, there is now a growing requirement to complement this approach with a more sophisticated and comprehensive analysis of data using a Central Statistical Monitoring (CSM) methodology. At CluePoints, we’re interested to know your thoughts on how companies are going to adopt these complementary approaches to ensure data accuracy and integrity in their trials?

Unlike the other Risk-Based Monitoring topics discussed over recent weeks, when we spoke with a number of our partners about this, the responses were quite wide-ranging, so we’ve tried to provide a quick overview for you in this post (for their full responses, please see here). We would love to hear your views and experiences on this matter too, so please feel free to get the discussion started!

The majority of our partners were in agreement that we are likely to see an increase in adoption of Central Statistical Monitoring across the industry over the coming years due to the advanced capabilities and value that the technology offers. Angie Maurer, Co-Founder & CEO at Zynapsys commented: “These systems deliver not only more transparent and efficient data but the sophisticated technology ensures data is also much more robust, so I think we will definitely see an increase in adoption.” An opinion echoed by Steve Young, Senior Director of Transformation Services at OmniComm Systems, who said: “Advanced Central Statistical Monitoring capabilities, that can identify outliers and unrealistic data patterns, are crucial. The industry is becoming more interested in this approach and as a result, I think adoption rates will increase significantly in the near future.”

Oracle Health Sciences’ Senior Director of Life Sciences Product Strategy, James Streeter, thinks that we are likely to see the use of Key Risk Indicators (KRIs) reduce over time, saying: “For now, what we are likely to see is companies adopting an approach which combines the use of Key Risk Indicators (KRIs) and Central Statistical Monitoring, to give them a broader picture of the potential risks within their clinical trials. As study teams begin to understand that Central Statistical Monitoring provides them with more access to real-world data, coupled with the increase in data being collected from emerging technology, the use of Key Risk Indicators (KRIs) will diminish.”

In terms of practical implementation, Adam Butler, Senior Vice President, Strategic Development & Corporate Marketing at Bracket Global urged sponsors to ensure they develop a strategy which addresses all of the potential risks that could arise when collecting data, and when doing this, utilize Risk-Based Monitoring and Central Statistical Monitoring as support tools. Jamie O’Keefe, Vice President, Life Sciences and R&D Practice at Paragon Solutions, thinks that we will see a divide between large and smaller companies, commenting: “It will be much more achievable for smaller organizations to integrate Central Statistical Monitoring and adopt this new approach across all of their trials. The challenge for big pharma companies will be changing existing complex infrastructure. Trying to drive this change through multi-million dollar, multi-year implementations, will be a huge task and have a significant impact on business.”

While Jamie thinks the adoption of Central Statistical Monitoring could be easier for smaller organizations, Angie Maurer thinks that budget will have an influence for smaller companies and would like to see providers develop a flexible platform that will allow even small start-up companies to afford this new technology.

Finally, Steve Young raised an interesting question about who within an organization should take responsibility for the management of Central Statitstical Monitoring. In Steve’s opinion, while data managers are not statisticians, they have the data analytics skills, so will be best equipped to work with the monitoring team to translate data into an appropriate remediation action.

In our own experience to date, it is evident that Central Statistical Monitoring is just as applicable for small Pharma as it is for Large Pharma and CROs. The ‘light checks’ offered by Key Risk indicators (KRI) functionality certainly provide companies with an operational tool to regularly check data and drive reduced monitoring activities. However, most of the companies that CluePoints works with have identified that they also want to also use an independent and objective approach to comprehensively interrogate the data. Using the KRI approach only focuses on 15-20 largely operational variables, whereas CSM leaves no stone unturned in comprehensively analyzing all the clinical data within a study. The result is as rigorous and scientifically validated health check of the study and identification of anomalies within the data that can be examined and course-corrected to ensure patient safety and improved accuracy and integrity. The regulators also favor this approach and the revised ICH E6 guideline document also helps sponsors and CROs alike to determine how Central Monitoring should be best approached. The gold medal position is for sponsors to harness the power of  both KRIs and Central Statistical Monitoring to ensure improvement and integrity of the data throughout their studies and reduced risk when it comes to submission.

Are the Regulatory Authorities Ready for the Adoption of Risk-Based Monitoring?

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Firstly, thanks to those of you who got involved with our recent discussion about current best practice in Risk-Based Monitoring, it was great to hear from you. Next, we asked our partners to tell us about how they think the regulatory authorities will respond to the adoption of Risk-Based Monitoring, and importantly, if they are ready for it.

With both the FDA and the EMA releasing draft guidance for the conduct of Risk-Based Monitoring, there is no doubt that a shift to this approach by sponsors and CROs will be welcomed by the authorities. However, when it comes to actually putting it into practice, our partners still have questions about whether those carrying out audits and inspections are in practice ready and willing to accept these new methodologies. James Streeter, Senior Director of Life Sciences Product Strategy at Oracle Health Sciences commented that despite the regulatory authorities pushing for widespread adoption of Risk-Based Monitoring, “in reality, it may take some time for individual auditors to accept the new Risk-Based Monitoring methodologies and how these differ between organizations,” and this could prove to be a potential hurdle for the industry.

In an industry typically slow to adapt to change, it is no surprise that some organizations are yet to respond to this guidance – perhaps because it is just that, guidance. Karen Fanouillere, Biostatistics Project Leader at Sanofi, gave reasoning for this, saying that, “while the authorities are definitely on board with Risk-Based Monitoring and already advising the industry to adopt this approach, they have yet to outline any specific recommendations or guidance on exactly how they would like to see it implemented.” Our partners were in general agreement that the introduction of the ICH (E6) Addendum later this year should provide some of the much-needed clarity and ‘specifics’ that the industry is waiting for, and will also force the hand of many organizations still resisting the change.

All that said, the majority of our partners are in regular discussions with the authorities and many are of the opinion that Risk-Based Monitoring methodologies which, as Dr. Peter Schiemann, Managing Partner, and Co-Founder, of Widler & Schiemann, said, “are based on data and facts, and follow a clear plan,” are likely to be accepted by the regulators. Steve Young, Senior Director of Transformation Services at OmniComm agreed with this view point, saying: “The door has been opened very clearly by the regulatory authorities to encourage the industry to move forward with Risk-Based Monitoring,” and, “as long as sponsors have a well-documented quality management plan that demonstrates how risk assessment was carried out, how the monitoring plan was guided by that risk assessment and makes clear the findings (and any remediations), then there should be few issues.”

From the CluePoints perspective, we can certainly see that the agencies, via the various guidance documents, are clearly insisting that sponsors and their partners adopt new centralized monitoring processes that will both improve data quality and reduce cost. The agencies themselves are also scrutinizing their own processes, and I wouldn’t mind betting that they will be adopting similar new techniques for selecting sites for inspection in the near future. The regulatory bodies have certainly given the industry the push that has been needed to affect change that will herald a new way of managing study conduct and ensuring data quality while reducing risk. At CluePoints, we use a very effective analogy to the airline industry in the late 1980s whereby that industry went through a comprehensive process re-engineering designed to reduce costs but also improve safety. The results were extraordinary, and the similarities to the challenges we now collectively face in Pharma are remarkable. Let us know if you’d like to hear more.

What are your experiences of this? Do you have any advice to share from your experiences with the regulators?

The Evolution of CluePoints’ SMART Engine

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Regarding CluePoints’ statistical approach to risk-based monitoring in clinical trials, we are often asked questions about sample size, such as: “How many subjects are needed per site to perform your analyses in an ongoing study?” “Do you have a minimum sample size recommendation for this approach?” “How can this be applied to trials with many centers and few patients in each center?”

Previously, our recommendation was at least 4 patients per center and 10 centers per study, although it was possible to group the data differently (e.g. by country or region) to allow sites with fewer patients to be included in the analysis. In the context of trials in rare diseases, where there are few patients per center but the case report form is extremely detailed, the system could potentially be useful with fewer centers and/or fewer patients per center.

ClueBot EvolutionHowever, ClueBot now has some exciting news to share. SMART has evolved and is now able to score and rank every single center, no matter their number of patients! CluePoints’ R&D team have updated the SMART engine and created a way to reduce the requirements while ensuring the accuracy and validity of the results.

For additional information and answers to more of our most frequently asked questions, please visit:

CluePoints’ Commentary – EMA Reflection Paper on Risk Based Quality Management in Clinical Trials

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The final version of the EMA Reflection Paper on Risk Based Quality Management in Clinical Trials is now available. This document, as well as the FDA Guidance, significantly benefits the industry with the on-going efforts to introduce and develop the risk-based monitoring (RBM) paradigm. The purpose of this reflection paper, as stated by the EMA is to “encourage and facilitate the development of a more systematic, prioritised, risk-based approach to quality management of clinical trials, to support the principles of Good Clinical Practice and to complement existing quality practices, requirements and standards.”

Sponsors should embrace RBM practices in their drug development programs as it provides the opportunity to improve data quality and integrity whilst mitigating risk. According to the EMA, “The current manner in which some elements of a quality system are implemented by sponsors and their agents (CROs etc.) is generally acknowledged to be time-consuming and constitutes a major proportion of the cost of development of medicines. In addition, the ICH GCP guideline was finalised in 1996 when clinical research was largely paper based, but the available technology and the approach to the conduct of clinical trials has evolved considerably in the meantime.” Much of the industry would agree that while capable of conducting high quality clinical trials, the current oversight process can be expensive and inefficient. The use of technology, such as central statistical monitoring (CSM), can help alleviate quality management issues by identifying risk and determining the integrity of clinical data throughout the drug development process.

Regarding central statistical monitoring, the final version of the EMA reflection paper does not differ much from the draft published two years ago, and essentially endorses the use of CSM. “There is potential to develop central monitoring systems using statistical methodology to monitor the quality of the trial conduct and data, with regular metrics reports and records produced that demonstrate the checks/activities that are being undertaken and that they are compliant with the defined monitoring strategy and procedures. This could lead to targeted on-site visits to address the issues that such visits are better placed to detect.” [EMA Reflection Paper Section 5.2, page 13]

The EMA also suggests to “define the metrics that will allow oversight of the trial.” At CluePoints, the metric used is the statistical significance of the differences found in the data from any given site as compared with all other sites. Central statistical monitoring, as implemented by CluePoints’ SMART™ engine, is an unsupervised and independent approach to risk-based monitoring – using all study data to carry out a large number of comprehensive tests in order to identify statistical outliers.

In light of the EMA’s recommendations, statistical monitoring methods are proving critical to identify risk and ensure data integrity. By implementing these techniques sponsors can not only reduce the cost of clinical trials, but can make better use of resources and optimize overall study success rates. To learn more about CluePoints and the SMART engine, please visit:

Risk-Based Management and the Difference Between Key Risk Indicators and Central Statistical Monitoring

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I’m back! Did you miss me? I’ve actually dropped by to share some exciting news.  A few months ago, my wonderful creator at CluePoints, Dr Marc Buyse, hosted a complimentary webinar with his friend Brian Nugent from Gilead Sciences. I wanted to share with you some of the information they discussed as I made my guest appearances throughout the presentation [My parents, components of a super-computer and a Gulfstream jet, would be so proud!]. Today, I am going to cover risk-based management and the difference between Key Risk Indicators and Central Statistical Monitoring.  

As many of you know, Risk-Based Monitoring has been a ‘hot topic’ for all of us in the industry for close to two years now. Our current paradigm for monitoring is to do routine site monitoring visits on a regular basis.  In this case, sponsors go to sites every 4 to 10 weeks and a lot of Source Document Verification (SDV) is undertaken – 100% SDV in many cases. This process typically accounts for 30% of a sponsor’s overall costs [Wow!]. Now, the new paradigm that we’d like to explore is a risk-based or, as I would describe it, an ‘intelligent’ approach to monitoring.

Two documents, the FDA Guidance for Industry and the EMA Reflection paper, are really groundbreaking in our industry and encourage the use of risk-based monitoring tools as well as increased efficiency in the form of reduced SDV.  The articles bring to our attention the fact that sponsors should be focusing or “targeting” their on-site monitoring activities.  One of the most common ways to do so is by remote monitoring. This means taking a look at the data off site. We are also referring to data management metrics and trending. This includes things such as key performance indicators, key risk indicators and key results indicators — all adding up to help sponsors focus on what to do with the sites and to be able to garner greater knowledge of what is going on in the data from a remote location. Then, of course, there is Central Statistical Monitoring, which is where CluePoints comes in. Sponsors have to look at their data in numerous ways used to target on-site monitoring visits. By doing so, the goal is to have much less than 100% SDV and improved data quality at significantly lower costs.

Abstracts from the FDA guidance

The FDA guidance recommends to “replace on-site monitoring for monitoring activities that can be done as well or better remotely; monitor data quality through routine review of submitted data in real-time.” This, of course, has been made possible through the implementation of Electronic Data Capture (EDC) that is commonly used in most clinical trials.  The FDA also says that you should “conduct analyses of site characteristics, performance metrics; target on-site monitoring by identifying higher risk clinical sites.” This is really what we mean by Key Risk Indicators. In addition, the FDA says that you should “conduct aggregated statistical analyses of study data to identify sites that are outliers relative to others and to evaluate individual subject data for plausibility and completeness.” – A.K.A., Central Statistical Monitoring.

The Difference Between Key Risk Indicators and Central Statistical Monitoring


Examples of KRIs

Study conduct
Actual accrual vs. target
% pts with protocol violations
% dropouts
AE rate
AE grade 3/4 rate
SAE rate
Treatment compliance
% dose reductions
% dose delays
Reasons for Rx stops
Data management
Overdue forms
Query rate
Query resolution time

In contrast with key risk indicators, central statistical monitoring tries to find why one site is different from the others. Computers that hold all of the clinical trial data can quite easily perform checks based on statistical algorithms to compare sites and detect different patterns in the data.  Traditionally, humans are not good at doing this which is why 100% manual SDV is not an appropriate method for checking data quality. This concept has been implemented by CluePoints using the Smart™ engine [that’s me, ClueBot!].  The SMART engine runs a large number of comprehensive statistical tests comparing each site with all of the other sites in order to identify statistical outliers. The idea behind this is that all variables are indicative of quality – so not just the Key Risk Indicators but every patient-related variable that has been collected in a clinical trial. When we run CluePoints on a data set in a clinical trial, we typically take all the data into consideration, whether it be lab data, clinical data, baseline data, or treatment outcomes; everything goes into this system and all data are deemed equally important for the purposes of checking their quality. The system uses assumption-free generic tests so that there is no assumption about the distribution of the variables. 

When the SMART engine is run, each test generates a p-value. For instance, if you have a trial with 100 sites collecting 300 variables and you run an average of 5 tests per variable, the number of tests you can run is actually 100x300x5, which is a very large number of p-values. With that in mind, we obviously need a way to simplify or summarize all of these p-values into a single unique score. This is done using another statistical algorithm to determine an individual score for each and every site involved in the specific study being analyzed. You can think of a site’s score as being the average p-value of that site as compared with all of the other sites. So if a site has a very extreme score, that means it has a very extreme p-value, and is very likely to truly differ from the all of the others in regards to the data submitted by the site. We display this information as a bubble plot:


Consequently, CluePoints identifies sites that are outliers and then guides the sponsor in its investigation as to why these sites differ from all of the others by looking at the data more closely. It is important to note that CluePoints uses the false discovery rate to adjust for testing multiplicity in order to ensure the outlying sites identified are truly statistically different and that the findings are not simply due to the play of chance.

To summarize, we have two very different approaches to determining risk in clinical trials.  One is based on key risk indicators and the other is statistical monitoring. If we contrast these two approaches, key risk indicators are applied because they focus on important known risk factors, for example, the proportion of AEs or SAEs collected. There is no question that the safety reporting in a clinical trial is a key risk indicator that must be looked at very carefully.  The challenge with Key Risk Indicators is that they are based on subjective choices. In contrast, Central Statistical Monitoring is an agnostic and independent approach. It doesn’t make any assumptions about what data are important. We have learned from experience that investigators typically take great care to report primary efficacy variables and safety variables quite well (in most cases!), but may be less attentive or perhaps more sloppy when reporting other variables.  As you all well know, in a clinical trial, everything that is collected should be worth collecting, and therefore worth checking – that is why Central Statistical Monitoring ensures that you are able to determine the quality and integrity of your data and ensure that you focus efforts on errant sites quickly and efficiently.

magnifyFor more information please view our webinar “Removing the Risk in Risk-Based Management” on-demand or visit Also, feel free to leave a question or comment for any of our subject matter experts below, contact CluePoints directly, or share this blog with a colleague. Thank you for being a part of the CluePoints community and remember, if you follow this blog you will be entered for a chance to win a Google Chromebook! Just enter your email address into the subscribe box at the top of this page.  See you again soon!

CluePoints Launches Powerful & Pragmatic Solution to Risk-Based Monitoring

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As biopharmaceutical sponsors search for the optimal approach to targeted monitoring and reduced  Source Data Verification (SDV), CluePoints introduces an Intelligent Statistical approach like no other.

A new software and service provider of Intelligent Statistical Monitoring solutions – CluePoints – has been launched to address the increasing industry need for
a pragmatic approach to enable the risk-based monitoring theory to become a reality.

CluePoints allows biopharmaceutical sponsors and CROs to identify signals in a clinical trial data set and make timely decisions about which sites to target for monitoring activities and Source Data Verification (SDV). As a result, corrective action can be taken early and sites reassessed periodically throughout the course of a study to ensure the quality and integrity of the data, enhance patient safety and, ultimately, reduce regulatory submission risk.

The launch is in response to recent industry guidance from the FDA and a reflection paper from the EMA encouraging sponsors to embrace an alternative to traditional on-site monitoring techniques and to explore reduced Source Data Verification (SDV) using a risk-based approach to monitoring.

The FDA’s “Guidance for Industry Oversight of Clinical Investigations — A Risk-Based Approach to Monitoring” calls for sponsors to “replace on-site monitoring activities for monitoring activities that can be done as well or better remotely” and to “target on-site monitoring by identifying higher risk clinical sites by performing monitoring activities that can only be accomplished using centralized processes [and to] conduct aggregated statistical analyses of study data to identify sites that are outliers relative to others and to evaluate individual subject data for plausibility and completeness”. These statements could have been written with CluePoints in mind as this is exactly the approach employed by the company.

Francois Torche, CEO of CluePoints, comments: “It has taken a considerable amount of time to build the CluePoints solution due to the complex array of statistical algorithms used but the result is a powerful engine that can be used in all late-stage clinical trials. Not only does CluePoints help improve data quality and integrity, it also has the potential to act as the engine to drive millions of dollars in cost savings via reduced monitoring and SDV.”

At the heart of the CluePoints solution is the SMART™ engine, comprising a comprehensive range of inter-connected statistical tests that make no distributional assumptions about the clinical data but, when aggregated together, highlight difficult to detect issues in the site results. This differs greatly from many of the other solutions in development across the industry that use some type of Key Risk Indicators (KRIs) since SMART™ processes all elements of the clinical data in a comprehensive manner, with no predetermination of risk. This results in an objective view of which sites exhibit outlying or discrepant data and, hence, allow on-site monitoring activities to be targeted to address those centers as a priority.

The brainchild of Harvard-trained biostatistician Marc Buyse, CluePoints has evolved from academic research to a full commercial entity over the course of the last ten years. Buyse is cited in the FDA’s RiskBased Monitoring Guidance for his work in detecting fraud in clinical trials using statistical modeling techniques (The Role of Biostatistics in the Prevention, Detection and Treatment of Fraud in Clinical Trials. Statistics in Medicine 18, 1999). It has taken the International Drug Development Institute (IDDI) a decade to perfect the statistical algorithms that comprise the SMART™ engine underpinning CluePoints. This research is endorsed by a consortium comprising GlaxoSmithKline Vaccines, the Institute of Statistics at Université Catholique de Louvain, and the Artificial Intelligence Research Laboratory of Université Libre de Bruxelles. The powerful and sophisticated process used to detect anomalies in site data is currently pending patent protection with the United States Patent and Trademark Office.

For further information about the CluePoints solution please visit