获益与风险平衡在临床试验中引起了人们的关注。为了全面评估获益与风险，越来越多地使用广义的成对比较来估算基于多个优先考虑结果的净收益。虽然先前的研究已经证明，结果之间的相关性影响净收益及其估计，但这种影响的方向和大小仍不清楚。在本研究中，我们通过理论和数值分析来研究两个二元或高斯变量之间相关性对真实净收益值的影响。我们还通过模拟和实际肿瘤临床试验数据的应用，探讨了存活和分类变量之间相关性对基于四种现有方法（Gehan、Péron、Gehan with correction和Péron with correction）估算净收益的影响，其中存在正确的截尾。我们的理论和数值分析揭示了，不同结果分布的相关性对真实净收益值产生了各种方向的影响。在二元终点方面，这个方向由一个阈值50%的简单规则控制，用于评估一个有利的结果。我们的模拟结果表明，在存在正确的截尾的情况下，基于Gehan或Péron评分规则的净收益估计可能会有相当大的偏差，并且这种偏差的方向和大小与结果相关性有关。最近提出的校正方法大大降低了这种偏差，即使在存在强烈的结果相关性的情况下也是如此。解释净收益及其估计时，应仔细考虑相关性的影响。 © 2023 The Authors。Statistics in Medicine, 由约翰威利和儿子有限公司出版。

Benefit-risk balance is gaining interest in clinical trials. For the comprehensive assessment of benefits and risks, generalized pairwise comparisons are increasingly used to estimate the net benefit based on multiple prioritized outcomes. Although previous research has demonstrated that the correlations between the outcomes impact the net benefit and its estimate, the direction and magnitude of this impact remain unclear. In this study, we investigated the impact of correlations between two binary or Gaussian variables on the true net benefit values via theoretical and numerical analyses. We also explored the impact of correlations between survival and categorical variables on the net benefit estimates based on four existing methods (Gehan, Péron, Gehan with correction, and Péron with correction) in the presence of right censoring via simulation and application to actual oncology clinical trial data. Our theoretical and numerical analyses revealed that the true net benefit values were impacted by the correlations in various directions depending on the outcome distributions. With binary endpoints, this direction was governed by a simple rule with a threshold of 50% for a favorable outcome. Our simulation showed that the net benefit estimates based on Gehan's or Péron's scoring rule could be substantially biased in the presence of right censoring, and that the direction and magnitude of this bias were associated with the outcome correlations. The recently proposed correction method greatly reduced this bias, even in the presence of strong outcome correlations. The impact of correlations should be carefully considered when interpreting the net benefit and its estimate.© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.