Clinical Trial Performance Metrics

The power of clinical trial performance metrics

As the saying goes, “If you can measure it, you can improve it.” In this article, we explore how this relates to site selection and monitoring for clinical trial sponsors, as well as to improving performance for sites.

A growing body of research has demonstrated that performance analytics can optimize clinical research efforts across multiple dimensions. Tracking the right performance metrics can help sponsors efficiently identify potential high-performing sites and also quickly address inefficiencies or bottlenecks through effective site monitoring during the trial. For trial sites themselves, clinical trial metrics can be used internally to improve process efficiency. In this article, we discuss the concepts of performance metrics and key performance indicators, and explore the various benefits of performance analytics for trial operations as well as how the practice is changing amidst new technological advancements.

What are clinical trial performance metrics or quality performance measures?

Clinical trial performance metrics are measures of a trial site’s operational performance.¹,²

Also known as clinical research site performance metrics, these indicators provide insight into efficiency, issues, and risks at each trial site. Clinical trial performance metrics can be used by both sponsors and trial sites to analyze site operations, evaluate performance, and make improvements when necessary.

What is a clinical KPI?

Clinical KPI refers to key performance indicators; specific operational measures that are deemed to be the most useful/insightful for monitoring and assessing the current operational performance of a trial site.

The term KPI is often used interchangeably with clinical trial performance metrics, although it has been suggested that KPIs differ slightly from modern performance analytics in that KPIs indicate what process needs to be assessed, while performance analytics can reveal the actual cause of the problem.3 Let’s take the issue of a low enrollment rate, for example. A traditional KPI may reveal that the enrollment rate is low, but might not offer further insight into the causes. Modern performance analytics might be able to provide sponsors with deeper insights into the reasons behind this low enrollment rate by leveraging more comprehensive data. In this example, performance analytics may reveal that the low enrollment rate seems to be due to strict inclusion criteria.

The utility of measuring site performance

There are several potential benefits for both sponsors and trial sites in measuring clinical performance metrics. In addition to facilitating streamlined site monitoring and assessments, they also support the development of actionable plans that can lead to improved trial outcomes.¹,²

For sponsors, well-selected performance metrics can improve the efficiency of the site selection stage, provide ongoing insights into trial operations while a study is underway, help with the quick identification of any inefficiencies, and support data-driven decision-making to address such inefficiencies, for example by reallocating resources, re-training staff, or re-calibrating on-site equipment/devices.

For sites, clinical trial performance metrics provide vital, easily-monitored insights into internal operations and can help managers distribute workloads effectively across teams, streamline processes, and identify areas where improvements need to be made. Moreover, consistent measurement of performance metrics allows sites to provide practical, real-world data and historical records to sponsors to act as proof of their capabilities and responses to site feasibility questionnaires.

So, what are some of the performance metrics commonly used to assess the efficiency of clinical research?

How to measure clinical performance: Examples of clinical performance measures and models

A variety of metrics can be used to measure a site’s operational performance, but it is important to focus on those that provide valuable, actionable insights. In other words, don’t get carried away measuring and monitoring everything just because it’s possible and relatively simple with modern technology. The monitoring operation should be designed to provide simplified, streamlined insights that are explicitly relevant to the goals of the sponsor and/or site, which can be acted upon quickly in order to produce effective improvements. Below, we discuss some key performance metrics that may be worth considering and possible ways to group them to simplify oversight - again, the final selection should be based on the metrics that are most relevant to the objectives at hand, with special attention given to high-risk areas/operations.

Categories of clinical trial performance metrics

Analyzing hundreds of individual metrics would quickly become overwhelming and could lead to more confusion than actual benefit. If there is a significant number of performance metrics you believe are worth monitoring, grouping them together may be a good idea to give some structure to the assessment and enable broader insights specific to certain areas/types of operations.

Broadly speaking, performance metrics can be grouped:

- into “standard” clinical metrics (applicable to various studies conducted by the same sponsor or site) and study-specific metrics (unique metrics that might only apply to a single trial)

- according to the stage of the trial (startup, site maintenance, and site close-out metrics)

- by the aspect of the trial being measured, such as patient safety or budget adherence.⁴,⁵

Standard clinical trial performance metrics versus study-specific metrics

Sponsors may find it helpful to establish a set of standardized quality metrics; those that can be measured across all sites to help assess and compare the performance of those sites, regardless of the study being conducted. There are many factors that are unchanging between different trials, so establishing standard quality performance indicators can save time and help solidify workflows.

However, there may be a handful of study-specific metrics which could be useful for capturing important yet unique aspects of an individual study. These can be updated on a per-trial basis to act as key performance indicators of uniquely important aspects, while still monitoring the standardized metrics to support inter-study comparisons and avoid the need to update all performance metrics for every study.

Grouping according to study phase: Startup, maintenance, and close-out metrics

Classifying clinical trial performance metrics according to the stage of the study can help sponsors conceptualize trial operations throughout the trial lifecycle. This approach is useful for identifying where bottlenecks or setbacks that lead to costly delays are occurring, and thus revealing which general aspects require attention for improving overall efficiency and shortening study timelines.

Examples of specific metrics include the following:

Startup metrics: cycle times for investigator recruitment, draft budget reception to finalization, site activation, IRB submission to approval, and other cycle times; regulatory approval rate. Generally, shorter cycle times for various aspects of study start-up indicate efficient internal processes and high degree of professionalism amongst site staff.³
Maintenance metrics: participant accrual/enrollment rate, screen failure rate, data quality (completeness of informed consent forms, data accuracy, data handling practices and site logs, etc.) compliance (i.e., absence of non-compliance), adherence to protocol, patients’ treatment adherence, drop-out/attrition rate, and timelines such as cycle time to database lock, etc.
Close-out metrics: last patient last visit (LPLV), time to site closure, follow-up rate, supply disposal, data cleanliness, document archival, submission of close-out report to IRB, etc.

Grouped by operational/functional concept: Patient safety, data quality, cycle times, budget adherence, etc.

Categorizing metrics by the various operational and functional concepts of trial operations can help you group common measures with the same units, such as money (e.g., dollars) and time (e.g., days). This may provide useful, broader oversight into performance as it relates to specific areas of activity. For example, perhaps the timeline was followed perfectly but data quality suffered as a result of rushed activities. In the case of monetary metrics, sponsors can assess how resources were allocated and spent, if site allocations were sufficient, and if there are any areas where costs could be cut for future studies.

Clinical trial performance metrics and KPIs examples

A 2018 study surveyed clinical research professionals to suggest a list of core performance metrics from amongst over 115 clinical research site performance metrics that had been used to track and assess study performance.5 The researchers hoped to provide some clarity in terms of what metrics may be considered globally important across various trials. As mentioned, it is important for sponsors and sites to carefully select performance metrics that are directly relevant to their study, whether in terms of operational efficiency, final outcomes, or otherwise. The following list includes some examples of specific clinical trial performance metrics:

1. Recruitment/enrollment indicators

Percentage of target enrollment numbers achieved
Total number of participants enrolled
Screen failure rate
Percentage of participants screened who were randomized
Percentage of eligible participants who consented

2. Retention statistics

Percentage of participants who completed the trial
Number or percentage of participants who withdrew consent
Percentage of enrolled participants lost to follow-up

3. Cycle times

Time from site activation to first enrollment
Time from IRB submission to approval
Time from draft budget received to final approval
Time taken to respond to site feasibility questionnaire

4. Data quality, compliance, and patient safety indicators

Percentage of participants with complete data
Percentage of participants with a query for primary outcome data
Number of adverse reactions per enrolled participant
Time to query resolution
Participants with at least one protocol compliance violation
Number of late visits
Number of major audit findings

The evolution of performance analytics alongside emerging technologies in clinical trials

Clinical trial performance metrics and performance analytics capabilities are evolving as clinical trials increasingly adopt technological tools such as electronic data capture (EDC), clinical trial management systems (CTMS), and clinical data management systems (CDMS).

Many of these solutions have advanced functionalities that allow sponsors and sites to readily visualize various performance metrics to gain deep insights into data quality, patient safety, compliance, and other factors, with minimal manual involvement.

Moreover, the adoption of these tech solutions has digitized many manual processes and improved communication across teams, which in many cases has improved site performance overall - and especially in aspects such as quality control and risk management.

The future of performance analytics in clinical trials is promising, with advanced capabilities arising within the landscape of technological advances, predictive modeling, machine learning, and artificial intelligence.⁶

Conclusion

Clinical trial performance metrics are insightful indicators that are useful for sponsors in terms of selecting high-performing sites and monitoring their performance, and for sites in terms of improving internal operational efficiency and accuracy. There are essentially innumerable performance metrics that could be monitored, but it’s important that only the most relevant are identified such that the performance monitoring strategy provides clear and actionable insights that support effective decision-making.

Sponsors and sites could consider categorizing clinical performance metrics to provide more structured oversight, as well as adopting digital solutions to enhance and streamline performance analytics capabilities by leveraging increased automation and systems integration. As new clinical trial models and possibilities continue to emerge and develop alongside new technologies, clinical trial performance metrics and analytics will become increasingly powerful; if employed intelligently, these advancements could result in enhanced patient safety while reducing study timelines and time-to-market for new therapies.