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

Q4 Medicine
Critical care explorations Pub Date : 2024-09-20 eCollection Date: 2024-10-01 DOI:10.1097/CCE.0000000000001152
Ziming Chen, Michael O Harhay, Eddy Fan, Anders Granholm, Daniel F McAuley, Martin Urner, Christopher J Yarnell, Ewan C Goligher, Anna Heath
{"title":"Statistical Power and Performance of Strategies to Analyze Composites of Survival and Duration of Ventilation in Clinical Trials.","authors":"Ziming Chen, Michael O Harhay, Eddy Fan, Anders Granholm, Daniel F McAuley, Martin Urner, Christopher J Yarnell, Ewan C Goligher, Anna Heath","doi":"10.1097/CCE.0000000000001152","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results and conclusions: </strong>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.</p>","PeriodicalId":93957,"journal":{"name":"Critical care explorations","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11419436/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Critical care explorations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1097/CCE.0000000000001152","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

Abstract

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.

临床试验中分析存活率和通气时间复合值策略的统计能力和性能。
背景:急性低氧血症呼吸衰竭患者死亡风险高,使用呼吸机时间长。干预措施通常旨在降低死亡率和缩短使用呼吸机的时间。将这些终点作为单一综合结果(存活天数和无通气时间)进行分析的方法有很多,但目前还不清楚哪种分析方法性能最佳。因此,我们的目标是确定在临床试验中具有最高统计能力的分析方法:通过统计模拟,我们比较了多种分析存活和无通气天数的方法:t 检验、Wilcoxon 秩和检验、Kryger Jensen 和 Lange 检验,以及比例几率模型、障碍-泊松模型和竞争风险模型。我们对以下 14 种情况进行了比较1)死亡率和通气时间的不同基线分布,这些数据基于急性低氧血症呼吸衰竭患者的登记数据;2)治疗对死亡率和通气时间的不同影响:所有方法都能很好地控制 1 类错误率(即避免出现假阳性结果)。当使用比例几率模型模拟数据时,t 检验和序数模型的相对功率最高(分别为 92% 和 90%),其次是竞争风险模型。当使用生存模型模拟数据时,竞争风险模型的效力最高(100% 和 92%),其次是 t 检验和十类序数模型。当治疗只影响死亡率和通气时间中的一项时,所有模型都很难检测出干预的效果。总的来说,最佳分析策略取决于治疗对存活率和通气时间的影响以及结果的基本分布。所评估的模型对治疗效果的解释各不相同,在选择分析模型时必须同时考虑统计能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
5.70
自引率
0.00%
发文量
0
审稿时长
8 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信