Evidence map›Paper›PMID 31312748›Full record

ArticleContemporary clinical trials communications2019

Comparison of an ordinal endpoint to time-to-event, longitudinal, and binary endpoints for use in evaluating treatments for severe influenza requiring hospitalization.

Ross L Peterson, David M Vock, Abdel Babiker, John H Powers, Sally Hunsberger, Brian Angus, Armando Paez, James D Neaton, INSIGHT FLU-IVIG study group

Open access · goldAbstract read
In one paragraph

Article in Contemporary clinical trials communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.2field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 20 citations in OpenAlex.

  1. Optimizing endpoints in early phase clinical trials of acute respiratory distress syndrome.American journal of respiratory and critical care medicine · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 2 countries.

Ross L PetersonUniversity of Minnesota School of Public Health, Division of Biostatistics, Minneapolis, MN, USA.
David M VockUniversity of Minnesota School of Public Health, Division of Biostatistics, Minneapolis, MN, USA.
Abdel BabikerMRC Clinical Trials Unit at University College London, London, UK.
John H PowersCollaborative Clinical Research Branch (CCRB), National Institute of Allergy and Infectious Diseases, Rockville, MD, USA.
Sally HunsbergerBiostatistics Research Branch (BRB), National Institute of Allergy and Infectious Diseases, Rockville, MD, USA.
Brian AngusNuffield Department of Medicine, Oxford University, Oxford, UK.
Armando PaezDivision of Infectious Diseases, Baystate Medical Center, University of Massachusetts Medical School - Baystate, Springfield, MA, USA.
James D NeatonUniversity of Minnesota School of Public Health, Division of Biostatistics, Minneapolis, MN, USA.
INSIGHT FLU-IVIG study group
University of Minnesota · USNational Institute of Allergy and Infectious Diseases · USUniversity College London · GBUniversity of Massachusetts Chan Medical School · USUniversity of Oxford · GB

Funding

Medical Research Council MC_UU_12023/22Medical Research Council MC_UU_12023/23Medical Research Council MC_UU_12023/29
6 · The paper itself

Abstract

BACKGROUND/

aimsThe Food and Drug Administration recommends research into developing well-defined and reliable endpoints to evaluate treatments for severe influenza requiring hospitalization. A novel 6-category ordinal endpoint of patient health status after 7 days that ranges from death to hospital discharge with resumption of normal activities is being used in a randomized placebo-controlled trial of intravenous immunoglobulin (IVIG) for severe influenza (FLU-IVIG). We compare the power of the ordinal endpoint under a proportional odds model to other types of endpoints as a function of various trial parameters.

methodsWe used closed-form analysis and empirical simulation to compare the power of the ordinal endpoint to time-to-event, longitudinal, and binary endpoints. In the simulation setting, we varied the treatment effect and the distribution of the placebo group across the follow-up period with consideration of adjustment for baseline health status.

resultsIn the analytic setting, ordinal endpoints of high granularity provided greater power than time-to-event endpoints when most patients in the placebo group had either naturally progressed to the category of hospital discharge by day 7 or were far from hospital discharge on day 7. In the simulation setting, adjustment for baseline health status universally raised power for the proportional odds model. Across different placebo group distributions of the ordinal endpoint regardless of adjustment for baseline health status, only time-to-event endpoints yielded higher power than the ordinal endpoint for certain treatment effects.

conclusionsIn this case study, the FLU-IVIG ordinal endpoint provided greater power than time-to-event, binary, and longitudinal endpoints for most scenarios of the treatment effect and placebo group distribution, including the target population studied for FLU-IVIG. The ordinal endpoint was only surpassed by the time-to-event endpoint when many patients in the placebo group were on the cusp of hospital discharge on day 7 and the follow-up period for the time-to-event endpoint was extended to allow for additional events. Our general approach for evaluating the power of several potential endpoints for an influenza trial can be used for designing other influenza trials with different target populations and for other trials in other disease areas.

Indexed as

Clinical trialsEndpointsOutcome assessmentsProportional odds modelStatistical power

Identifiers

PMID31312748
PMCPMC6609815
OpenAlexW2950733939

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.