Graduate School of Medicine, Hokkaido University, Sapporo, Japan.
Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan.
Stat Med. 2023 May 10;42(10):1606-1624. doi: 10.1002/sim.9690. Epub 2023 Feb 27.
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.
获益-风险平衡在临床试验中受到越来越多的关注。为了全面评估获益和风险，广义的成对比较越来越多地被用于基于多个优先结局来估计净获益。尽管先前的研究已经表明结局之间的相关性会影响净获益及其估计值，但这种影响的方向和程度尚不清楚。在这项研究中，我们通过理论和数值分析研究了两个二项或高斯变量之间的相关性对真实净获益值的影响。我们还通过模拟和实际肿瘤学临床试验数据的应用，探讨了在存在右删失的情况下，生存和分类变量之间的相关性对基于四种现有方法（Gehan、Péron、Gehan 校正和 Péron 校正）的净获益估计值的影响。我们的理论和数值分析表明，真实的净获益值受到结局分布的不同方向的相关性的影响。对于二分类结局，这种方向由一个简单的规则控制，对于有利结局的阈值为 50%。我们的模拟表明，在存在右删失的情况下，基于 Gehan 或 Péron 评分规则的净获益估计值可能会出现显著的偏倚，并且这种偏倚的方向和程度与结局相关性有关。最近提出的校正方法即使在存在强烈的结局相关性的情况下，也能大大减少这种偏倚。在解释净获益及其估计值时，应该仔细考虑相关性的影响。
