Randomized Clinical Trials Using a Hierarchical Composite Primary End Point

Importance: Hierarchical composite end points (HCEs) are a promising tool used in randomized clinical trials (RCTs) to integrate multiple outcomes of varying clinical relevance into a single measure. Objective: To describe how often HCEs are used as primary outcomes in RCTs, and how they are constructed, analyzed, and reported. Evidence Review: MEDLINE, Embase, CENTRAL, and Web of Science were searched on December 9, 2024, complemented by a forward citation search of methodological papers on HCEs. RCTs that used an HCE as their primary outcome were included, defined either by self-declaration (hierarchical composite or outcome ranking) or through use of an HCE-specific analytical approach (win ratio, win odds, probabilistic index, or generalized pairwise comparison). Pilot studies, post hoc analyses, and hierarchically tested coprimary end points were excluded. Data were independently screened and extracted in duplicate. Trial characteristics, end point composition, hierarchy justification, and analytical methods were summarized descriptively. Findings: Among 5188 screened records, 92 RCTs were included, with 79 567 planned participants. The use of an HCE as a primary end point has increased, with 72 trials (78.3%) initiating recruitment within the past decade. Most RCTs were drug trials (43.5% [40 of 92]), in cardiology (43.5% [40 of 92]), multicenter (91.3% [84 of 92]), and non-industry sponsored (67.4% [62 of 92]). The 92 HCEs had a median (IQR) of 4 (3-5) components and the highest ranked component was usually mortality (80.4% [74 of 92]). The last hierarchical component was most often a continuous component (64.1% [59 of 92]). The majority of RCTs did not report any information on how the hierarchy was established (82.2% [60 of 73]; excluding RCTs where only information from registries was available). Generalized pairwise comparison was the most frequent analysis approach (57.3% [26 of 45]) among the 45 published RCTs, yet no standardized way for presenting results was observed. Conclusions and Relevance: This scoping systematic review of 92 RCTs using HCEs found that their use has increased across medical fields, but their construction, analytical approaches, and reporting of results remained highly heterogeneous. By systematically mapping how HCEs are currently implemented, further review is essential for the development of much needed methodological and reporting standards.

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Journal
JAMA Network Open
Published
2026-07-28
DOI
https://doi.org/10.1001/jamanetworkopen.2026.25935
Primary Topic
Statistical Methods in Clinical Trials
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article
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article

Randomized Clinical Trials Using a Hierarchical Composite Primary End Point

Nasreen Hassoun‐Kheir, Benjamin Speich, Christian Appenzeller‐Herzog, Holly Jackson et al.
JAMA Network Open
Statistical Methods in Clinical Trials
article

Randomized Clinical Trials Using a Hierarchical Composite Primary End Point

Nasreen Hassoun‐Kheir, Benjamin Speich, Christian Appenzeller‐Herzog, Holly Jackson, Michaela Schumacher, Nadine Saskia Bernasconi, Selina Ehrenzeller, Nina Khanna, Steven Y. C. Tong, Yonas Martin, M.P.M. Hensgens, Natalie Rose, Sean W. X. Ong, Marlieke E. A. de Kraker, Matthias Briel, Amos J. de Jong, Richard Kuehl
article en

Abstract

Importance: Hierarchical composite end points (HCEs) are a promising tool used in randomized clinical trials (RCTs) to integrate multiple outcomes of varying clinical relevance into a single measure. Objective: To describe how often HCEs are used as primary outcomes in RCTs, and how they are constructed, analyzed, and reported. Evidence Review: MEDLINE, Embase, CENTRAL, and Web of Science were searched on December 9, 2024, complemented by a forward citation search of methodological papers on HCEs. RCTs that used an HCE as their primary outcome were included, defined either by self-declaration (hierarchical composite or outcome ranking) or through use of an HCE-specific analytical approach (win ratio, win odds, probabilistic index, or generalized pairwise comparison). Pilot studies, post hoc analyses, and hierarchically tested coprimary end points were excluded. Data were independently screened and extracted in duplicate. Trial characteristics, end point composition, hierarchy justification, and analytical methods were summarized descriptively. Findings: Among 5188 screened records, 92 RCTs were included, with 79 567 planned participants. The use of an HCE as a primary end point has increased, with 72 trials (78.3%) initiating recruitment within the past decade. Most RCTs were drug trials (43.5% [40 of 92]), in cardiology (43.5% [40 of 92]), multicenter (91.3% [84 of 92]), and non-industry sponsored (67.4% [62 of 92]). The 92 HCEs had a median (IQR) of 4 (3-5) components and the highest ranked component was usually mortality (80.4% [74 of 92]). The last hierarchical component was most often a continuous component (64.1% [59 of 92]). The majority of RCTs did not report any information on how the hierarchy was established (82.2% [60 of 73]; excluding RCTs where only information from registries was available). Generalized pairwise comparison was the most frequent analysis approach (57.3% [26 of 45]) among the 45 published RCTs, yet no standardized way for presenting results was observed. Conclusions and Relevance: This scoping systematic review of 92 RCTs using HCEs found that their use has increased across medical fields, but their construction, analytical approaches, and reporting of results remained highly heterogeneous. By systematically mapping how HCEs are currently implemented, further review is essential for the development of much needed methodological and reporting standards.

JAMA Network OpenVol. 9(7)
University of Bern (CH), Sunnybrook Health Science Centre (CA), The University of Melbourne (AU), University of Basel (CH), University of Toronto (CA), University Hospital of Bern (CH), Health Sciences Centre (CA), University Hospital of Basel (CH), University Medical Center Utrecht (NL), Peter Doherty Institute (AU), University Hospital of Geneva (CH), Geneva College (US)
Openalex Percentile: Top 6%
Statistical Methods in Clinical Trials
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