Identifying Important Risk Indicators in Clinical Development

Key risk indicators (KRIs) are metrics designed to identify risks of specific interest to study teams and considered an essential component of centralized monitoring. The results presented here are compiled from studies for which one or more KRIs are implemented, and only those initiated on the risk-based quality management (RBQM) platform since 2020 to focus on the most current trends.* This comprises 294 studies contributed by 32 different organizations.

The average total number of KRIs implemented per study is 18. There is some variability around this average. For example, 25% of the studies have at least 23 KRIs, and another 25% have 14 or fewer KRIs.

Table 1 below summarizes the 10 most commonly used KRIs by the percentage of studies in which they have been observed. KRIs topping the list include screen failure rate, adverse event (AE) reporting rate (both serious and non-serious), and electronic case-report form (eCRF) visit-to-entry cycle time—and at least seven of the top 10 are confirmed to be used in a majority of studies. It should be noted that these KRIs were identified with the support of a machine learning algorithm that selected KRIs with names and descriptions that closely matched those presented in the table. It is, therefore, likely that some additional KRIs exist that are similar in purpose to these 10 but are not reflected in the percentages.

While there is a fair amount of variability in the average percentage of sites in each study that had risk alerts generated for each KRI, it is clear from our research that sites are most frequently alerted for risks related to delays in eCRF query response (15.8%) and eCRF data entry (12.5%). This is understandable given that most study teams choose to set discrete risk thresholds (e.g., average cycle time > 10 days) for these two KRIs, while other KRIs more often employ statistical or “relative” thresholds (e.g., p-value < 0.05). The use of relative thresholds, which are most often set based on a p-value < 0.05, will typically limit the number of sites flagged within a given study to 5% or less.

* This is the first installment in a planned series presenting industry insights and trends related to implementing risk-based quality management (RBQM). The insights presented are compiled from the CluePoints RBQM platform, which includes information from more than 1,000 studies contributed by 88 research organizations.

Steve Young, chief scientific officer, and Sylviane de Viron, data and knowledge manager; both with CluePoints