ArticleClinical trials (London, England)2017
Analysis of an ordinal endpoint for use in evaluating treatments for severe influenza requiring hospitalization.
Article in Clinical trials (London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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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.
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.
Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating whether the proportional odds models to analyse ordinal outcomes in COVID-19 clinical trials is providing clinically interpretable treatment effects: A systematic review.Clinical trials (London, England) · 2024Pooled it
- Pooled it
- Efficacy and Safety of Sarilumab in Hospitalized Patients With Coronavirus Disease 2019: A Randomized Clinical Trial.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022Trial
- The Activin/FLRG Pathway Associates with Poor COVID-19 Outcomes in Hospitalized Patients.Molecular and cellular biology · 2022Trial
- Baseline Severe Acute Respiratory Syndrome Viral Load Is Associated With Coronavirus Disease 2019 Severity and Clinical Outcomes: Post Hoc Analyses of a Phase 2/3 Trial.The Journal of infectious diseases · 2021Trial
- Interferon β-1b in treatment of severe COVID-19: A randomized clinical trial.International immunopharmacology · 2020Trial
- Trial
- A Randomized Clinical Trial of the Efficacy and Safety of Interferon β-1a in Treatment of Severe COVID-19.Antimicrobial agents and chemotherapy · 2020Trial
- Anti-influenza hyperimmune intravenous immunoglobulin for adults with influenza A or B infection (FLU-IVIG): a double-blind, randomised, placebo-controlled trial.The Lancet. Respiratory medicine · 2019Trial
- Optimizing endpoints in early phase clinical trials of acute respiratory distress syndrome.American journal of respiratory and critical care medicine · 2026Article
- A Bayesian quasi-likelihood design for identifying the minimum effective dose and maximum utility dose in dose-ranging studies.Statistical methods in medical research · 2024Article
- Statistical analyses of ordinal outcomes in randomised controlled trials: protocol for a scoping review.Trials · 2023Article
- Effectiveness of Fortified Garlic Extract Oral Capsules as Adjuvant Therapy in Hospitalized Patients with Coronavirus Disease 2019: A Triple-Blind Randomized Controlled Clinical Trial.Current therapeutic research, clinical and experimental · 2023Article
- Article
- Influenza Therapeutics in Clinical Practice-Challenges and Recent Advances.Cold Spring Harbor perspectives in medicine · 2021Review
- New Perspectives on Antimicrobial Agents: Remdesivir Treatment for COVID-19.Antimicrobial agents and chemotherapy · 2020Review
- SARS-COV2 infection in 30 HIV-infected patients followed-up in a French University Hospital.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2020Article
- Assessing via Simulation the Operating Characteristics of the WHO Scale for COVID-19 Endpoints.Statistics in biopharmaceutical research · 2020Article
- Endpoints for randomized controlled clinical trials for COVID-19 treatments.Clinical trials (London, England) · 2020Review
- Comparison of an ordinal endpoint to time-to-event, longitudinal, and binary endpoints for use in evaluating treatments for severe influenza requiring hospitalization.Contemporary clinical trials communications · 2019Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Background/Aims A single best endpoint for evaluating treatments of severe influenza requiring hospitalization has not been identified. A novel six-category ordinal endpoint of patient status is being used in a randomized controlled trial (FLU-Intravenous Immunoglobulin - FLU-IVIG) of intravenous immunoglobulin. We systematically examine four factors regarding the use of this ordinal endpoint that may affect power from fitting a proportional odds model: (1) deviations from the proportional odds assumption which result in the same overall treatment effect as specified in the FLU-IVIG protocol and which result in a diminished overall treatment effect, (2) deviations from the distribution of the placebo group assumed in the FLU-IVIG design, (3) the effect of patient misclassification among the six categories, and (4) the number of categories of the ordinal endpoint. We also consider interactions between the treatment effect (i.e. factor 1) and each other factor. Methods We conducted a Monte Carlo simulation study to assess the effect of each factor. To study factor 1, we developed an algorithm for deriving distributions of the ordinal endpoint in the two treatment groups that deviated from proportional odds while maintaining the same overall treatment effect. For factor 2, we considered placebo group distributions which were more or less skewed than the one specified in the FLU-IVIG protocol by adding or subtracting a constant from the cumulative log odds. To assess factor 3, we added misclassification between adjacent pairs of categories that depend on subjective patient/clinician assessments. For factor 4, we collapsed some categories into single categories. Results Deviations from proportional odds reduced power at most from 80% to 77% given the same overall treatment effect as specified in the FLU-IVIG protocol. Misclassification and collapsing categories can reduce power by over 40 and 10 percentage points, respectively, when they affect categories with many patients and a discernible treatment effect. But collapsing categories that contain no treatment effect can raise power by over 20 percentage points. Differences in the distribution of the placebo group can raise power by over 20 percentage points or reduce power by over 40 percentage points depending on how patients are shifted to portions of the ordinal endpoint with a large treatment effect. Conclusion Provided that the overall treatment effect is maintained, deviations from proportional odds marginally reduce power. However, deviations from proportional odds can modify the effect of misclassification, the number of categories, and the distribution of the placebo group on power. In general, adjacent pairs of categories with many patients should be kept separate to help ensure that power is maintained at the pre-specified level.
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