What are hierarchical clinical endpoints (HCEs)?

Hierarchical clinical endpoints (HCEs) are a method used in clinical trials to evaluate the effectiveness of treatments by prioritizing different outcomes based on their clinical importance. Unlike traditional composite endpoints, which treat all components equally, HCEs rank outcomes to reflect their significance, ensuring that more critical events are given higher priority.

Why are HCEs important in clinical trials?

HCEs provide a more accurate representation of treatment effects. HCEs help avoid the pitfalls of traditional composite endpoints by considering the clinical priority of each outcome. This approach can lead to better decision-making and more reliable results.

How do HCEs differ from traditional composite endpoints?

Traditional composite endpoints combine multiple outcomes into a single measure, treating each component equally. This can produce misleading results if the components vary significantly in clinical importance. For example, consider a composite endpoint composed of time-to-death and time to hospitalization. If one analyzes the time to first occurrence, only the first hospitalization will be taken into consideration, even if a death occurs later. To avoid failing to capture deaths occurring after hospitalization in a trial, a hierarchy must be defined for the composite endpoint. By ranking outcomes hierarchically, sponsors can ensure that more critical events, like death, are prioritized over less severe ones, such as hospitalization. This hierarchy helps capture the true impact of the treatment on patient outcomes.

How are HCEs analyzed in clinical trials?

HCEs are analyzed using methods such as win ratio, win odds, and win difference. These non-parametric statistics compare all pairs of treated and control patients based on the hierarchy of endpoints. The win ratio, for example, represents the odds of a treated patient having a favorable outcome compared to a control patient.

What are the benefits of using HCEs in clinical trials?

The benefits of HCEs include: Improved accuracy: By prioritizing clinically significant outcomes, HCEs provide a clearer picture of treatment effects. Enhanced power: Including quantitative outcomes into an HCE, like biomarkers, can resolve ties between a treated and a control and increase the power of the analysis. Better decision-making: HCEs can help to assess the benefit-risk ratio more effectively, facilitating regulatory approvals and informed clinical decisions. The graphical representations of HCEs allow you to rapidly get an initial idea of the results for each component.

Are there any challenges associated with HCEs?

Yes, there are several challenges with HCEs, such as: Complexity: Designing and analyzing HCEs can be more complex than traditional endpoints. Sample size estimation: Determining the sample size for HCEs requires time-consuming simulations. Missing data: Managing missing data in HCE analyses can be computationally demanding.

How does the FDA view HCEs?

Dr. Barbara Gillespie, MD, MMS, FASN, VP and Therapeutic Head of Nephrology at Fortrea, attended an invitation-only closed meeting of nephrologists sponsored by the NKF and FDA in Jan 2025 to discuss the potential role of HCEs in renal trials to achieve Food and Drug Administration (FDA) approvals. “Statisticians presented how cardiologists have been using HCEs and Win Ratios to describe the effects of CV therapies, and the audience of nephrologists look forward to determining which future kidney clinical trials should embrace HCEs. We also focused on how to further educate our community, including our patients, on the value of HCEs in our studies.” - Barbara Gillespie, MD, MMS, FASN She learned that the FDA recognizes the importance of HCEs and recommends clear descriptive presentations of results for each component. However, post-hoc use of HCEs should be interpreted cautiously and cannot establish effectiveness if the trial was not conclusive.

How do you see HCEs changing renal research?

Given that the use of HCEs offers a more nuanced and accurate assessment of treatment effects, our team at Fortrea predicts that we will see: Enhanced statistical power and efficiency: HCEs integrate multiple outcomes into a single prioritized hierarchy, which can increase the statistical power of clinical trials without any assumption on the statistical model as it works with non-parametric statistics. This means that researchers can detect meaningful treatment effects with smaller sample sizes, making trials more efficient and potentially faster. Improved benefit-risk assessment: By prioritizing clinically significant outcomes, HCEs provide a clearer picture of the benefit-risk ratio of new treatments. This can lead to more informed decision-making in both clinical practice and regulatory approvals, ensuring that the most beneficial treatments reach patients more quickly. Comprehensive outcome measurement: HCEs allow for the inclusion of both hard clinical endpoints (like death and dialysis) and continuous measures (such as eGFR slope). This comprehensive approach captures the full spectrum of disease progression and treatment effects, leading to more robust and nuanced insights into renal disease management.

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