Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2025 Dec 24;81(5):e319-e329.
doi: 10.1093/cid/ciaf314.

Making Sense of Hierarchical Composite End Points in Randomized Clinical Trials-A Primer for Infectious Diseases Clinicians and Researchers

Sean W X Ong  1   2   3   4 , Robert K Mahar  5   6   7 , Chris J Selman  6   8 , Ruxandra Pinto  1   3 , Joshua S Davis  9   10   11 , Robert A Fowler  1   3 , Steven Y C Tong  2   4 , Nick Daneman  1   3
Affiliations
Review

Making Sense of Hierarchical Composite End Points in Randomized Clinical Trials-A Primer for Infectious Diseases Clinicians and Researchers

Sean W X Ong et al. Clin Infect Dis. .

Abstract

Hierarchical composite end points (HCEs), combining features of simple composite end points and conventional ordinal end points, are increasingly being used in infectious diseases (ID) research. However, many clinicians may be unfamiliar with these novel end points, including the variety of different target parameters that may be of interest and the methods that can be used to estimate them. In this review, we provide a conceptual overview of HCEs by defining them and providing examples from the ID literature. We explain different methods for analyzing HCEs, including (1) the Wilcoxon rank sum approach (often used in studies with a desirability of outcome ranking [DOOR] end point), (2) generalized pairwise comparisons (used to estimate a win ratio or win odds), (3) proportional odds model (and the relevance of the proportional odds assumption), and, (4) the probabilistic index model. This review will help ID clinicians and healthcare providers interpret current and future research using such end points.

Keywords: Hierarchical composite end points; clinical trial; ordinal outcomes; outcomes; trial design.

PubMed Disclaimer

Conflict of interest statement

Potential conflicts of interest. The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

Figures

Figure 1.
Figure 1.
Examples of ordinal end points, composite end points, and hierarchical composite end points. Italicized acronyms indicate example trials that use the stated example end point. Abbreviations: COVID-19, coronavirus disease 2019; WHO, World Health Organization.
Figure 2.
Figure 2.
Example of a generalized pairwise comparisons analysis in a hypothetical clinical trial. In this hypothetical example clinical trial, 20 patients are randomized to 2 treatment groups—groups A and B. The simplified hierarchical composite end point depicted here consists of 2 outcomes in order of priority: (1) death and (2) complication. Each patient in one group is compared with each patient in the other (total 10 × 10 = 100 comparisons) and determined to be a win, loss, or tie depending on the outcomes. The total number of wins, losses, and ties are added up, which permits calculation of the win ratio, win odds, and net treatment benefit. Abbreviation: SOC, standard of care.
Figure 3.
Figure 3.
Illustration of the proportional odds model (PO), in settings with a consistent effect across the ordinal scale (A) and a discordant effect at different ends of the ordinal scale (B). Abbreviation: OR, odds ratio.
Figure 4.
Figure 4.
Relationships among choice of end points, target parameters, and analytic methods. Arrows between end points and target parameters represent the possible target parameters that can be calculated or estimated from each end point, and arrows between analytic methods and target parameters represent the output target parameters from each analytic method. Arrows between target parameters show that the different parameters can be estimated or approximated from each other.
See this image and copyright information in PMC

References

    1. Selman CJ, Lee KJ, Ferguson KN, Whitehead CL, Manley BJ, Mahar RK. Statistical analyses of ordinal outcomes in randomised controlled trials: a scoping review. Trials 2024; 25:241. - PMC - PubMed
    1. Ong SWX, Petersiel N, Loewenthal MR, Daneman N, Tong SYC, Davis JS. Unlocking the DOOR-how to design, apply, analyse, and interpret desirability of outcome ranking endpoints in infectious diseases clinical trials. Clin Microbiol Infect 2023; 29:1024–30. - PubMed
    1. Evans SR, Rubin D, Follmann D, et al. Desirability of outcome ranking (DOOR) and response adjusted for duration of antibiotic risk (RADAR). Clin Infect Dis 2015; 61:800–6. - PMC - PubMed
    1. Hardy M, Harris PNA, Paterson DL, Chatfield MD, Mo Y; MERINO Trial Investigators . Win ratio analyses of piperacillin-tazobactam versus meropenem for ceftriaxone-nonsusceptible Escherichia coli or Klebsiella pneumoniae bloodstream infections: post hoc insights from the MERINO trial. Clin Infect Dis 2024; 78:1482–9. - PMC - PubMed
    1. Daneman N, Rishu A, Pinto R, et al. ; BALANCE Investigators, for the Canadian Critical Care Trials Group, the Association of Medical Microbiology and Infectious Disease Canada Clinical Research Network, the Australian and New Zealand Intensive Care Society Clinical Trials Group, and the Australasian Society for Infectious Diseases Clinical Research Network . Antibiotic treatment for 7 versus 14 days in patients with bloodstream infections. N Engl J Med 2024; 392:1482–9.

MeSH terms