Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany.
Crit Care Med. 2021 Jan 1;49(1):e11-e19. doi: 10.1097/CCM.0000000000004741.
Many trials investigate potential effects of treatments for coronavirus disease 2019. To provide sufficient information for all involveddecision-makers (clinicians, public health authorities, and drug regulatory agencies), a multiplicity of endpoints must be considered. The objectives are to provide hands-on statistical guidelines for harmonizing heterogeneous endpoints in coronavirus disease 2019 clinical trials.
Randomized controlled trials for patients infected with coronavirus disease 2019.
General methods that apply to any randomized controlled trial for patients infected with coronavirus disease 2019.
Coronavirus disease 2019 positive individuals.
None.
We develop a multistate model that is based on hospitalization, mechanical ventilation, death, and discharge. These events are both categories of the ordinal endpoint recommended by the World Health Organization and also within the core outcome set of the Core Outcome Measures in Effectiveness Trials initiative for coronavirus disease 2019 trials. To support our choice of states in the multistate model, we also perform a brief review of registered coronavirus disease 2019 clinical trials. Based on the multistate model, we give recommendation for compact, informative illustration of time-dynamic treatment effects and explorative statistical analysis. A majority of coronavirus disease 2019 clinical trials collect information on mechanical ventilation, hospitalization, and death. Using reconstructed and real data of coronavirus disease 2019 trials, we show how a stacked probability plot provides a detailed understanding of treatment effects on the patients' course of hospital stay. It contributes to harmonizing multiple endpoints and differing lengths of follow-up both within and between trials.
All ongoing clinical trials should include a stacked probability plot in their statistical analysis plan as descriptive analysis. While primary analysis should be on an early endpoint with appropriate capability to be a surrogate (parameter), our multistate model provides additional detailed descriptive information and links results within and between coronavirus disease 2019 trials.
许多试验研究了治疗 2019 年冠状病毒病（COVID-19）的潜在效果。为了向所有相关决策者（临床医生、公共卫生当局和药物监管机构）提供足够的信息，必须考虑多种终点。目标是为 2019 年冠状病毒病临床试验中协调异质终点提供实用的统计指南。
针对感染 2019 年冠状病毒病患者的随机对照试验。
适用于任何感染 2019 年冠状病毒病患者的随机对照试验的通用方法。
感染 2019 年冠状病毒病的个体。
无。
我们开发了一种多状态模型，该模型基于住院、机械通气、死亡和出院。这些事件既是世界卫生组织推荐的有序终点的类别，也是 2019 年冠状病毒病疗效试验核心结局测量倡议的核心结局集的一部分。为了支持我们在多状态模型中选择状态，我们还对已注册的 2019 年冠状病毒病临床试验进行了简要回顾。基于多状态模型，我们为动态治疗效果的简明、信息丰富的说明和探索性统计分析提供建议。大多数 2019 年冠状病毒病临床试验都收集了关于机械通气、住院和死亡的信息。使用 2019 年冠状病毒病试验的重建和真实数据，我们展示了堆叠概率图如何提供对治疗效果对患者住院过程的详细了解。它有助于协调多个终点和不同的随访时间，无论是在试验内还是试验间。
所有正在进行的临床试验都应在其统计分析计划中包括堆叠概率图作为描述性分析。虽然主要分析应该是具有适当替代能力的早期终点（参数），但我们的多状态模型提供了额外的详细描述性信息，并在 2019 年冠状病毒病试验内和之间链接结果。
