دورية أكاديمية
أعرض في EDS

Statistical Power and Performance of Strategies to Analyze Composites of Survival and Duration of Ventilation in Clinical Trials.

التفاصيل البيبلوغرافية
العنوان: Statistical Power and Performance of Strategies to Analyze Composites of Survival and Duration of Ventilation in Clinical Trials.
المؤلفون: Chen Z; Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada., Harhay MO; Department of Biostatistics, Epidemiology and Informatics Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA., Fan E; Department of Medicine, Division of Respirology, University Health Network, Toronto, ON, Canada., Granholm A; Department of Intensive Care, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark., McAuley DF; School of Medicine, Dentistry and Biomedical Sciences, Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, United Kingdom.; Regional Intensive Care Unit, Royal Victoria Hospital, Belfast, United Kingdom., Urner M; Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, ON, Canada.; Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada., Yarnell CJ; Department of Medicine, Division of Respirology, University Health Network, Toronto, ON, Canada.; Department of Critical Care Medicine, Scarborough Health Network, Toronto, ON, Canada.; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada., Goligher EC; Department of Biostatistics, Epidemiology and Informatics Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.; Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada.; Department of Physiology, University of Toronto, Toronto, ON, Canada.; Toronto General Hospital Research Institute, Toronto, ON, Canada., Heath A; Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.; Department of Statistical Science, University College London, London, United Kingdom.
المصدر: Critical care explorations [Crit Care Explor] 2024 Sep 20; Vol. 6 (10), pp. e1152. Date of Electronic Publication: 2024 Sep 20 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Wolters Kluwer Health Country of Publication: United States NLM ID: 101746347 Publication Model: eCollection Cited Medium: Internet ISSN: 2639-8028 (Electronic) Linking ISSN: 26398028 NLM ISO Abbreviation: Crit Care Explor Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Philadelphia, PA : Wolters Kluwer Health, [2019]-
مواضيع طبية MeSH: Respiratory Insufficiency*/therapy , Respiratory Insufficiency*/mortality , Respiration, Artificial*/mortality , Clinical Trials as Topic*/methods, Humans ; Time Factors ; Survival Analysis ; Models, Statistical
مستخلص: Background: Patients with acute hypoxemic respiratory failure are at high risk of death and prolonged time on the ventilator. Interventions often aim to reduce both mortality and time on the ventilator. Many methods have been proposed for analyzing these endpoints as a single composite outcome (days alive and free of ventilation), but it is unclear which analytical method provides the best performance. Thus, we aimed to determine the analysis method with the highest statistical power for use in clinical trials.
Methods: Using statistical simulation, we compared multiple methods for analyzing days alive and free of ventilation: the t, Wilcoxon rank-sum, and Kryger Jensen and Lange tests, as well as the proportional odds, hurdle-Poisson, and competing risk models. We compared 14 scenarios relating to: 1) varying baseline distributions of mortality and duration of ventilation, which were based on data from a registry of patients with acute hypoxemic respiratory failure and 2) the varying effects of treatment on mortality and duration of ventilation.
Results and Conclusions: All methods have good control of type 1 error rates (i.e., avoid false positive findings). When data are simulated using a proportional odds model, the t test and ordinal models have the highest relative power (92% and 90%, respectively), followed by competing risk models. When the data are simulated using survival models, the competing risk models have the highest power (100% and 92%), followed by the t test and a ten-category ordinal model. All models struggled to detect the effect of the intervention when the treatment only affected one of mortality and duration of ventilation. Overall, the best performing analytical strategy depends on the respective effects of treatment on survival and duration of ventilation and the underlying distribution of the outcomes. The evaluated models each provide a different interpretation for the treatment effect, which must be considered alongside the statistical power when selecting analysis models.
Competing Interests: Dr. Harhay is funded by the Patient-Centered Outcomes Research Institute (Award ME-2020C1-19220) and the U.S. National Institutes of Health/National Heart, Lung, and Blood Institute (grant numbers R00-HL141678 and R01-HL168202). Dr. Urner is supported by a Scholarship of the Interdepartmental Division of Critical Care Medicine, University of Toronto. Dr. Heath is funded by a Canada Research Chair in Statistical Trial Design; Natural Sciences and Engineering Research Council of Canada (award No. RGPIN-2021 03366). Dr. Goligher is funded by an Early Career Investigator Award from the National Sanitarium Association. The remaining authors have disclosed that they do not have any potential conflicts of interest.
(Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)
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معلومات مُعتمدة: R00 HL141678 United States HL NHLBI NIH HHS; R01 HL168202 United States HL NHLBI NIH HHS; Scholarship Interdepartmental Division of Critical Care Medicine
تواريخ الأحداث: Date Created: 20240920 Date Completed: 20240920 Latest Revision: 20240925
رمز التحديث: 20240925
مُعرف محوري في PubMed: PMC11419436
DOI: 10.1097/CCE.0000000000001152
PMID: 39302988
قاعدة البيانات: MEDLINE
الوصف
تدمد:2639-8028
DOI:10.1097/CCE.0000000000001152