white paper: from here to risk-based monitoring

From Here to Risk-Based Monitoring A framework and checklists for a successful transition

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White Paper: From Here to Risk-Based Monitoring

Contents1. Understanding Risk-Based Monitoring Solutions in Full   1

Limitations of Previous Comparisons 1The Importance of Real-Time, Forward-Looking Management 1Suggested Framework for Comparison 2

2. Monitoring Each Study on its Own Terms  2

The Starting Point: a Risk-Based Monitoring Plan 2The Importance of Midstudy Adjustments 3

3. Looking Backward: Error Detection and Correction  4

Introduction 4Audits 4Central Statistical Monitoring 4Focused Continuous Central Monitoring 5The Old Standby: Source Data Verification 6

4. Monitoring in the Moment: Real-Time Quality Management  7

Quality Metrics 7Site Performance Metrics 7Key Risk Indicators 7Fixed Performance Indices 7The Speed of Monitoring Adjustments 8

5. Ensuring Future Success: Error Prediction and Prevention  9

Beyond Reactive Monitoring 9Error Prediction Based on On-Screen Data Checks 9Dynamic Performance Indices 9Indirect Leading Indicators 10

6. Planning the Right Transition for You  10

Implementing a Solution In-House 10Selecting an Outsourced Solution 11

7. Summing Up 12

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1. Understanding Risk-Based Monitoring Solutions in Full

Limitations of Previous ComparisonsData quality, patient safety and optimal resource allocation are the primary goals of risk-based monitoring. In the two years since publication of the relevant EMA reflection paper and FDA guidance, methodologies, products and services for risk-based monitoring have proliferated and diversified. The biopharmaceutical industry can choose from a variety of general approaches and specific practices to achieve the desired goals. This article provides a framework to assist biopharma and medical device companies in selecting and implementing a risk-based monitoring approach or selecting a service provider based on offerings in the marketplace. A comparison of specific risk-based monitoring products and services is beyond the scope of this article.

Most comparisons of risk-based monitoring approaches have focused overwhelmingly on source-data verification (SDV) – what percentage of different types of information on which subjects and site activities must be verified or reviewed and how. The approach to SDV is central to any monitoring approach, risk-based or otherwise. However, as befits a process that is largely unchanged from the era of paper-based clinical trials, SDV is mostly about detection and correction of errors after the fact. Focusing the discussion of risk-based monitoring on SDV tends to understate other important aspects of risk-based monitoring such as proactive quality management and error prevention.

The Importance of Real-Time, Forward-Looking ManagementIn the author’s view, a comparison of risk-based monitoring approaches should devote substantial attention to prospective features that enable proactive management to ensure that each study meets goals for data quality, patient safety and optimal resource allocation. Error detection and correction remain essential but the most effective risk-based monitoring approaches shift the primary focus from the past to the present and future. Accurate understanding of current study status is vital. Therefore, a risk-based monitoring approach should track data quality and site performance on a basis as close to real-time as possible. In addition to proactive management based on current information, risk-based monitoring should include anticipatory management based on predictive modeling.

Some may think extending the comparison of risk-based monitoring approaches in this manner ventures beyond monitoring and into trial management. That is true. However, technology makes the extension unavoidable. The distinction between monitoring and trial management was clear in an age of paper trials, but the advent of electronic data capture (EDC) and Electronic Clinical Trial Management Systems (eCTMS) has unified once separate activities. In the electronic age, capabilities for tracking and predicting data quality and site performance are as essential as SDV to achieving the goals of risk-based monitoring. Therefore, this paper presumes that a comparison of risk-based monitoring approaches should encompass upstream and forward-looking activities that affect data quality.

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Suggested Framework for ComparisonThis article compares risk-based monitoring approaches and practices in the following areas:

• Tailoring the Approach for Each Study

• Looking Backward: Correcting and Detecting Past Errors

• Monitoring in the Moment: Real-Time Quality Management

• Ensuring Future Success: Error Prediction and Prevention

• Making the Transition: Issues in Adoption and Implementation

This paper takes the view that SDV, while extremely important, is one form of error correction and detection.

2. Monitoring Each Study on its Own TermsFDA guidance stresses the importance of performing a risk assessment to identify issues specific to the trial prior to authoring a risk-based monitoring plan. Such a plan foresees the need to adjust monitoring activities based on circumstances observed during the trial. However, problems may arise in areas where risks initially seemed low. For this reason, individualization is important during the trial as well as in the planning stage.

The Starting Point: a Risk-Based Monitoring PlanRisk-based monitoring approaches typically set allowable error rates for tiers of subjects, track the number of errors and determine whether monitoring adjustments are required to keep the incidence of errors in check. For example, tiered monitoring approaches often rely on tables from QA practice that specify the number of acceptable errors for critical data on subjects: 1-10, 11-100 and >101. This is a reasonable starting point for a monitoring plan.

However, a risk-based monitoring plan should identify the specific issues and risks involved in a study and especially the potential effects of different errors on the final statistical analysis and regulatory submissions. The study team must assess the likely effect of each type of error on the final analysis as well as the worst-case effect. Errors in a certain field related to a primary endpoint may be of paramount importance. The magnitude of errors or variability of data in fields related to a primary endpoint may affect the reliability of results just as the incidence of errors does. The need to understand the likely and worst-case effects of different factors on the final analysis explains why biostatisticians play such an important role in risk-based monitoring.

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At a minimum, a risk-based monitoring plan should define the following:

• Critical data

• Key Risk/Quality/Performance Indicators (KRIs/KQIs/KPIs)

• Potential error types and associated remedial actions and action triggers

• Allowable error rates

• Initial SDV approach, including an algorithm for selecting subjects and data to SDV and % SDV for subject groups, data types, ICFs, study procedures, etc.

• The basis for adjusting the SDV approach during the study

• The basis for scheduling monitoring visits and adjusting the visit schedule

Section 4 provides detailed information on a variety of metrics to consider when authoring a risk-based monitoring plan. For simplicity, this document will use “Key Risk Indicators” to denote measures that industry practices sometimes calls Key Quality Indicators or Key Performance Indicators.

The Importance of Midstudy Adjustments

Optimal monitoring requires adjusting to actual circumstances observed during a study. If the available CTMS includes an integral adjustment mechanism, the initial monitoring plan may define how such a mechanism will apply to the current study. Other types of substantial changes in the monitoring approach should be reflected in amendments to the monitoring plan.

When a CTMS supports use of Key Risk Indicators, most monitoring approaches utilize the initially identified set of KRIs throughout a study. However, an alternative approach offers greater flexibility, allowing adjustments to KRIs during the trial based on continuous assessment of the predictive value of each indicator for actual site performance and data quality. Such adjustments may change weights for each component metric or extend to replacing one metric initially designated as a KRI with another that has proven a better predictor of site performance and data quality. To support such flexibility in KRIs, a risk-based monitoring system may incorporate a model that assesses the predictive power of various metrics, enabling changes in indices and dashboards that continually focus the study team on the activities that have proved most important to the success of the trial.

Checklist 1: Individualization for the Specific Study

At startup, including monitoring plan

• Risk assessment, including risk to analysis

• Choice of metrics ̶ Derived from past trials? ̶ Specific to study?

• Flag critical fields?

• Set Acceptable Quality Levels/ error rates for each: ̶ Primary endpoint? ̶ Secondary endpoint? ̶ Data Field?

Adjustments during trial

• Monitoring basics ̶ Frequency ̶ Intensity ̶ Monitoring Method

» Central (remote) » Onsite » Mixed

• SDV targeting (see separate table)

• Metrics ̶ Change Key Risk Indicators? ̶ Change index components and

weighting? ̶ Correlate indices and

components with actual quality/performance?

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3. Looking Backward: Error Detection and Correction

IntroductionThe management utility of methods of error detection and correction depends on time of utilization. The most definitive and accurate way to detect errors is by examining and analyzing the entire study database at study close. However, the only management adjustment possible at that stage is to avoid a regulatory submission based on low-quality data. At the other extreme, there is not enough data early in a study’s earliest days to support many approaches to error detection and correction. Thus, time of utilization is central to any assessment of methods for error detection and correction.

AuditsWhile it is important to detect errors before a regulatory submission, error detection during an audit shortly before database lock may come too late to save a study. Such an audit may produce information on site performance that is as definitive as a medical examiner’s determination of the cause of death. Audits are included here because a risk-based monitoring plan likely includes triggers for audits. Audits are clearly all about error detection rather than correction.

Central Statistical MonitoringAs the FDA risk-based monitoring guidance suggests, central statistical monitoring is a more effective way to identify some types of errors than onsite SDV and is a wise precaution as reliance on remote monitoring increases. The question about central statistical monitoring is not whether it can identify errors but when. Central statistical monitoring packages have the disadvantage of requiring substantial amounts of trial data. Such packages typically operate in batch mode, perhaps running only a single time after collecting 80% of trial data or at the very end. Such packages have the advantage of being able to analyze a high percentage of data and provide a valuable safety net to detect anomalies in unmonitored data. This may allow correcting problems in planned regulatory submissions or prevent a submission that is doomed from the outset because of quality issues. Like late audits, late central statistical monitoring may provide only an authoritative postmortem for the current study. If results are favorable, central statistical monitoring can increase confidence in earlier monitoring of the trial by other means and in a planned regulatory submission.

In principle, central statistical monitoring could contribute to trial management through more frequent runs. However, the value of the analysis depends on the volume of data available, likely preventing the use of central statistical monitoring as a tool for managing a study to meet quality goals.

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Focused Continuous Central MonitoringThe central monitoring that matters most to the success of risk-based monitoring identifies problems continuously from the outset and provides a basis for rapid reaction to emerging issues. This type of central monitoring is continuous in the sense that members of the study team perform a variety of checks every day, not that all types of checks are performed continuously. Focused continuous central monitoring can include rules-based analytic checks, cross-CRF consistency comparisons, checks for outliers, checks of dates vs. treatment windows and many other types of checks.

Some central monitoring processes are useful from the first patient visit, such as those that detect inconsistencies between CRFs on the same subject or between subjects for the same visit. A single date for a patient visit that occurs outside a treatment window may reflect a data entry error or a serious issue in site performance. Flagging an out-of-window visit allows immediate investigation to identify and correct the problem. In some instances, it is necessary to establish a baseline for central checks. However, it is often possible to establish a useful baseline early in a study based on the first few subjects at each site.

The keys to focused continuous central monitoring are to:

• Identify as many potential problems as possible in advance

• Establish processes for detecting such problems

• Maintain relentless vigilance for signs of any developments or events that might compromise data quality.

Rules-based analytic monitoring encapsulates experience with previous trials. Each rule reflects a previously observed pattern associated with a specific type of error. For example, a rule may flag repeated values for vital signs for a single subject or repeated values for all subjects on a single date, both highly unlikely to result from actual measurements. The value of central rules-based analytic monitoring depends on:

• Completeness of the library of rules

• Applicability of the rules to the current study

• Frequency and timeliness of the rules-based analysis.

As with central statistical monitoring, the value of rules-based analytic monitoring as a tool for achieving quality goals is limited unless the analysis occurs early and often. Inclusion of rules-based monitoring in this section rests on the assumption that rules-based checks will happen frequently or continuously.

Checklist 2 summarizes key elements and considerations in central monitoring.

Checklist 2: Central Monitoring Approaches

Central statistical monitoring

• Pattern detection

• Deviation detection

• Minimum data requirements?

• Frequency?

Focused central monitoring

• Rules-based ̶ Rules-libraries available? ̶ Frequency?

Study-specific data checks

Validations vs. protocol requirements

Standard DM checks

Cross-CRF consistency comparisons

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The Old Standby: Source Data VerificationThis section concerns the range of SDV approaches supported by a risk-based monitoring system or service. As noted in the introduction, existing comparisons of risk-based monitoring approaches typically focus on SDV approach. Key dimensions of SDV are the selection algorithm and the granularity of selection. In some instances, features such as specific selection algorithms and field tagging may be native to a trial-management or EDC system. However, it is also possible for biostatisticians to define and implement SDV algorithms in collaboration with the study management team. If implementing a risk-based monitoring product in-house, native support for such features may be particularly important in the absence of internal staff with the capabilities and time to implement desired algorithms and field tagging.

Common approaches to SDV include random selection of data on tiers of subjects and declining SDV after early monitoring demonstrates acceptable error rates. A purely random approach ignores the central principle of identifying areas that pose the greatest risk to data quality and focusing monitoring efforts accordingly.

Checklist 3: Representative SDV Parameters

Selection algorithms

• Triggered

• Targeted

• Random

• Declining

• Tiered

• Mixed

Granularity

• By site

• By subject

• By CRF

• By field

• By endpoint

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4. Monitoring in the Moment: Real-Time Quality ManagementImmediate feedback is central to improving any process. Because feedback from SDV is far from immediate, effective real-time quality management must utilize a variety of study-specific Key Risk Indicators as a basis for controlling factors that play an important role in ensuring data quality, patient safety and efficient resource allocation.

Quality MetricsThe ability to set target Acceptable Quality Levels (AQL) for data related to an endpoint or for a specific field and then to track actual quality continuously during the trial provides a foundation for achieving quality goals. This approach incorporates tracking of such measures of variability of data for individual fields or for the set of fields that define a primary or secondary endpoint. Metrics of this type focus monitoring attention in areas of greatest importance to the analysis and regulatory submissions.

Streaming information is ideal for effective management to achieve quality goals. If management relies on periodic reports, errors can escape detection and proliferate between reports. Table 4 summarizes metrics useful in real-time quality management as well as some important considerations involved in selecting such metrics.

Site Performance MetricsExperience shows that sites that are slow to enroll patients, enter data from patient visits or resolve queries are more likely to produce low-quality data. Site performance metrics like those provided in Checklist 5 enable the study team to identify potential problem sites and intervene early to prevent a compromise of data quality.

Key Risk IndicatorsKey Risk Indicators (KRIs) (also called Key Performance Indicators or Key Quality Indicators) are measures of error rates and data quality that are designated as important for management of the current study. The value of KRIs is availability before accumulation of sufficient data for meaningful measures of parameters such as error rates and variability. Some KRIs, such as query rates and time to query resolution, apply universally. Study planners may identify additional KRIs based on experience with previous trials in the same therapeutic area or, preferably, based on the initial risk assessment for the current study.

Checklist 4: Metrics for Real-Time Quality

Management Real-time streaming or periodic?

• If streaming, currency

• If periodic, frequency

Data quality levels vs AQL by:

• Field

• Critical field

• Endpoint

• Subject tier

• Site

• Region

• Monitor

Queries/100 fields

Primary endpoint queries

Critical field queries

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Fixed Performance IndicesPerformance indices combine selected Key Risk Indicators to reflect site performance on activities related to data quality. Some systems utilize the same performance indices for all studies. Others define performance indices during startup, often based on historical information, and leave the indices unchanged throughout a study, with both the component measures and weights for each fixed. Fixed performance indices are a valuable basis for study management. However, dynamic indices based on the actual predictive value of component measures are far more valuable. Section 5 covers dynamic performance indices.

The Speed of Monitoring AdjustmentsPeriodic Adjustments

Management based on period reports is an artifact of the age of paper-based studies. Even today, many management approaches rely on monthly or quarterly reports. With such long reporting cycles, errors may escape detection for long periods. Worse yet, similar errors may proliferate before the study team realizes that there is a problem. Another common problem with periodic reports is that they often provide data tables that are a starting point for analysis rather than a basis for action.

Continuous Adjustments

The ideal process for error detection and correction is continuous rather than periodic, utilizing streaming information for early detection of problems and precise planning to enable immediate response. The ability to make continuous adjustments depends on:

• Specifications of clear decision criteria (triggers) in the monitoring plan

• Specification of specific remedial actions for foreseeable types of quality issues

• Availability of actionable information (as opposed to raw data or tabular reports) relating to triggers

• Readiness to execute predefined responses

• A feedback loop to track the effectiveness of responses and make further adjustments if necessary

• Vigilance for signs of unforeseen issues

• For some types of data, accumulation of sufficient information for meaningful interpretation.

Checklist 5: Site-Performance Metrics for Real-Time

Management Real-time streaming or periodic?

• If streaming, currency

• If periodic, frequency

Observational or actionable?

Common metrics:

• Time to query resolution

• Time from patient visit to CRF entry

• Protocol deviations

• Protocol deviation under/over reporting

• AEs under/over reporting (by comparison with study median)

• SAE queries

• AE queries

• Time to SAE reporting

• Enrollment rate

• Screenfail rate

• Discontinue rate

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5. Ensuring Future Success: Error Prediction and Prevention

Beyond Reactive MonitoringWhile essential, the query process as an industry institution promotes a mentality that accepts errors as inevitable and focuses on piecemeal corrections. While a continuous process of error detection and correction improves on customary reliance on periodic reports, a continuous process for error prediction and prevention enables a leap beyond a reactive monitoring model, shifting the focus from error correction to prevention. Such a process enables the study team to investigate suspected issues immediately and, upon confirmation of a problem, to intervene before an issue can compromise study data. Error prediction heightens and focuses vigilance, enabling the study team to correct errors at the source and prevent recurrences, keeping data as clean and accurate as possible throughout a trial. This not only minimizes error and variability, but also increases efficiency by reducing the need for corrective actions. However, emphasis on error prevention requires an effective mechanism for error prediction.

Error Prediction Based on On-Screen Data ChecksThe frequency of data-entry errors rejected by on-screen data checks may provide an early indication of site performance problems. By definition, errors rejected at this stage do not reach the study database. However, information on range-check failures is available immediately and thus has management value if it turns out that such failures track with real site performance issues. On the other hand, inaccurate keyboarding by designated site personnel may NOT reflect an investigator’s ability to ensure proper performance of study procedures and collection of accurate data on eligible subjects. For this reason, results of on-screen data checks may not be a reliable indicator of actual or future site performance.

Dynamic Performance IndicesIdeally, a Site Performance Index is composed of elements that are in fact predictive of site performance. While this is the goal with fixed performance indices, the selected component measures and weights do not always prove predictive. A model that assesses the predictive value of a range of candidate metrics enables adjustments to both the components and weights of a performance index. This tunes the performance index to the realities of the current study and provides a basis for reports and dashboards that focus the study team on the factors that are proving most important to data quality and study success. In effect, dynamic performance indices provide continuous updates to the initial risk assessment, thus focusing monitoring activities on actual rather than expected risks and providing trending information and a basis for predicting future sources of error. Typical risk-based monitoring is only as good as the initial

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risk assessment. Risk-based monitoring based on dynamic performance indices ensures that risk-based monitoring is functioning as intended.

Indirect Leading IndicatorsAnother approach to obtaining indications of site performance as early as possible is identification of indirect or surrogate measures of site performance. When such indirect indicators are available continuously and are available without the need for monitoring visits, they become valuable management tools for achieving quality goals.

6. Planning the Right Transition for YouCompanies face a choice between in-house implementation and outsourcing. Each involves challenges. In-house implementation must contend with the need to evolve existing systems or adopt new ones. While outsourcing clinical trials allows a choice among many CROs, relatively few CROs offer robust risk-based monitoring during the current transitional period.

Implementing a Solution In-HouseIn-house implementation of risk-based monitoring presents substantial challenges whether adopting and implementing new systems or evolving in-house systems developed before the current emphasis on risk-based monitoring. Immediate use of risk-based monitoring on in-house systems may require accepting substantial functionality limitations in systems not designed for the purpose. A fuller implementation will likely take substantial time and investment. Implementing a homegrown system provides the opportunity to focus on individual company needs and preferences but is likely the most costly and time-consuming approach.

When choosing commercial software integrated in an EDC system or eCTMS, features in the current release of the software determine the possibilities in the short term. If the current implementation of the chosen package lacks desired features, the customer must persuade the developer to include the desired functionality in a future release. That release

Checklist 6: Metrics for Error Prediction and Prevention

Rules of thumb

• Errors at on-screen data checks

Dynamic performance indices

• Consist of most predictive elements proportionally weighted

• Components adjusted based on actual predictivity

• Weights adjusted based based on actual predictivity

• Provide trending and predictive information

Indirect measures

• Available without visiting site?

• Known independently of site reporting?

• Predictive of direct measures

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may become available months or years after recognition of the need for additional features. Furthermore, a commercial software vendor will likely prioritize revisions based on the size and importance of customers and the number of customers requesting similar functionality.

Furthermore, the transition to any new technology always brings the risk of picking the wrong product or vendor. It is common during technology transitions to invest in a product that proves less capable than expected or a vendor that makes design choices that prove suboptimal as the technology and market evolve. This argues for a thorough, deliberate selection process and a strategy of maintaining as much flexibility as possible.

Selecting an Outsourced SolutionAs with most complex software products, the process for obtaining risk-based monitoring through outsourcing is much easier than in-house adoption and implementation. It is necessary to develop an understanding of the potential possibilities, pitfalls and benefits, to author an appropriate RFP and to identify CROs with the capability to propose and deliver solutions. Implementation is someone else’s problem.

One of the advantages of outsourcing during a technology transition is the opportunity to learn and the flexibility to change vendors based on initial experience. If the initial experience is disappointing, the next RFP can reflect lessons learned and target additional or different service providers.

Depending on the match between CRO capabilities and identified monitoring needs, outsourcing may enable immediate adoption of fully functional risk-based monitoring. Outsourcing is likely the preferred path for small pharma and biotechs. Indeed, such companies may realize a competitive advantage during the transition to risk-based monitoring. While larger pharma companies and many CROs and CTMS companies struggle with the challenges of in-house technology migration, companies unencumbered by existing infrastructure can identify nimble partners that promise the benefits of risk-based monitoring. Then it is a matter of ensuring that the selected partner delivers the promised value.

Checklist 7: Issues in Adoption and Implementation

In-House Implementation

• Identify desired feature set

• Compare offerings

• Select commercial package

• Workarounds for missing features?

• Timeline

• Installation ̶ Process development ̶ Pilot project ̶ Revisions based on lessons learned ̶ Deployment ̶ Staff training ̶ Cultural transformation from 100% SDV ̶ Maintream use

Outsourced

• Identify CROs with: ̶ Full understanding of risk-based approaches ̶ Appropriate technology ̶ Track record ̶ Trained staff ̶ Completed cultural transformation ̶ Ability to individualize to sponsor and study

• Send RFP

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7. Summing UpRisk-based monitoring is an important advance with the potential to promote substantial improvements in data quality, patient safety and resource allocation. However, risk-based monitoring approaches must contend with a mindset developed in the age of paper trials. In these early days of the transition to risk-based monitoring, pharma companies, CTMS vendors and CROs are typically adopting rudimentary quality-management-based approaches that are likely to produce modest improvements over historical monitoring approaches that relied almost entirely on SDV. In many cases, the limitations of existing systems, the complexity and cost of required technology upgrades and the challenges of cultural transformation are leading to cautious approaches with modest benefits that may actually increase costs without producing substantial improvements in data quality.

To reap the full benefits of risk-based monitoring, the industry must leverage the best of modern management approaches and computing technology. This requires a shift to management based on real-time metrics, precise focus on the information most critical to study success and predictive elements that allow anticipatory rather than reactive or even proactive management techniques. While larger companies navigate a tortuous migration path from 100% SDV for all trials to the right monitoring approach for each study based on its unique requirements, smaller companies can enjoy substantial competitive advantage by adopting products or services from partners that are leading the transition to effective risk-based monitoring.

Insight and Assistance

Health Decisions provides a range of information and services to assist companies in the transition to risk-based monitoring. This includes:

• Presentations to address sponsor inquiries about risk-based monitoring• In-depth consultation on potential risk-based monitoring solutions • Full study execution based on a proven risk-based monitoring technology and processes utilized by a staff with

great experience and thorough training in risk-based monitoring techniques.

To discuss how Health Decisions can assist you with the transition to risk-based monitoring, please contact us at [email protected].

About Health Decisions Health Decisions is an industry-leading clinical research organization (CRO) and pioneer of Agile Clinical Development, providing proactive trial management based on real-time actionable information, enabling decisions that reduce timelines, accelerate access to new medicines and medical devices and allow biopharmaceutical and device companies to seize market opportunities. Founded in 1989, Health Decisions has conducted a range of successful clinical trials involving tens of thousands of patients, consistently reducing sponsors’ financial risk and time to market. Health Decisions is headquartered in Durham, NC and operates on five continents.

Health Decisions 2510 Meridian Pkwy. Durham, NC 27713 Tel: +1.919.967.1111 Toll Free: +1.888.779.3771 Email: [email protected] For more information, please visit www.HealthDec.com or engage our CEO, Michael Rosenberg, MD, MPH, on his blog, Trials Without Tribulations. ©2013 Health Decisions. All rights reserved. All brand names are trademarks or registered trademarks of their respective company.

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