The assessment of safety is an important aspect of the evaluation of new therapies in clinical trials, with analyses of adverse events being an essential part of this. Standard methods for the analysis of adverse events such as the incidence proportion, that is the number of patients with a specific adverse event out of all patients in the treatment groups, do not account for both varying follow-up times and competing risks. Alternative approaches such as the Aalen-Johansen estimator of the cumulative incidence function have been suggested. Theoretical arguments and numerical evaluations support the application of these more advanced methodology, but as yet there is to our knowledge only insufficient empirical evidence whether these methods would lead to different conclusions in safety evaluations. The Survival analysis for AdVerse events with VarYing follow-up times (SAVVY) project strives to close this gap in evidence by conducting a meta-analytical study to assess the impact of the methodology on the conclusion of the safety assessment empirically. Here we present the rationale and statistical concept of the empirical study conducted as part of the SAVVY project. The statistical methods are presented in unified notation, and examples of their implementation in R and SAS are provided.

中文翻译：

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AdVerse 事件随随访时间变化 (SAVVY) 的生存分析：荟萃分析研究的基本原理和统计概念

安全性评估是临床试验中新疗法评估的一个重要方面，不良事件分析是其中的重要组成部分。不良事件分析的标准方法，例如发生率比例，即治疗组中所有患者中发生特定不良事件的患者人数，没有考虑不同的随访时间和竞争风险。已经提出了替代方法，例如累积关联函数的 Aalen-Johansen 估计量。理论论证和数值评估支持这些更先进方法的应用，但据我们所知，这些方法是否会在安全评估中导致不同结论的经验证据不足。具有不同随访时间的 AdVerse 事件生存分析 (SAVVY) 项目旨在通过进行元分析研究来根据经验评估该方法对安全评估结论的影响，从而缩小这一证据差距。在这里，我们介绍作为 SAVVY 项目一部分进行的实证研究的基本原理和统计概念。统计方法以统一的符号表示，并提供了它们在 R 和 SAS 中的实现示例。