Giai Joris, Maucort-Boulch Delphine, Ozenne Brice, Chiêm Jean-Christophe, Buyse Marc, Péron Julien
Hospices Civils de Lyon, Pôle Santé Publique, Service de Biostatistique - Bioinformatique, Lyon, France.
Laboratoire de Biométrie et Biologie Évolutive, Équipe Biostatistique-Santé, University of Lyon; University Lyon 1; CNRS; UMR 5558, Villeurbanne, France.
Stat Med. 2021 Feb 10;40(3):553-565. doi: 10.1002/sim.8788. Epub 2020 Nov 2.
The prioritized net benefit (Δ) is a measure of the benefit-risk balance in clinical trials, based on generalized pairwise comparisons (GPC) using several prioritized outcomes. Its estimation requires the classification as Wins or Losses of all possible pairs of patients, one from the experimental treatment (E) group and one from the control treatment (C) group. In this simulation study, we assessed the impact of the correlation between prioritized outcomes on Δ, its estimate, bias, size, and power.
The theoretical Δ value was derived for the specific case of two correlated binary outcomes when a normal copula is used. Focusing on one efficacy and one toxicity outcome, two situations frequently met in practice were simulated: binary efficacy outcome with binary toxicity outcome, or time to event efficacy outcome with categorical toxicity outcome. Several scenarios of efficacy and toxicity were generated, with various levels of correlation.
When E was more effective than C, positive correlations were mainly associated with a decrease in the proportion of Losses, while negative correlations were associated with a decrease in the proportion of Wins on the toxicity outcome. This resulted in an increase of with the intensity of the positive correlation without adding any bias. Results were similar whatever the type of outcomes generated but led to power alteration.
Correlations between outcomes analyzed with GPC led to substantial but predictable modifications of Δ and its estimate. Correlations should be taken into consideration when performing sample size estimations in clinical trials.
优先净效益（Δ）是基于使用多个优先结局的广义成对比较（GPC）来衡量临床试验中效益-风险平衡的指标。其估计需要将所有可能的患者对进行分类，一类来自试验治疗（E）组，另一类来自对照治疗（C）组，并区分为胜或负。在本模拟研究中，我们评估了优先结局之间的相关性对Δ及其估计值、偏差、大小和效能的影响。
当使用正态copula时，针对两个相关二元结局的特定情况推导理论Δ值。聚焦于一个疗效结局和一个毒性结局，模拟了实践中经常遇到的两种情况：二元疗效结局与二元毒性结局，或事件发生时间疗效结局与分类毒性结局。生成了几种具有不同相关水平的疗效和毒性情景。
当E比C更有效时，正相关主要与负比例的降低相关，而负相关与毒性结局上胜比例的降低相关。这导致在不增加任何偏差的情况下，随着正相关强度的增加而增大。无论生成的结局类型如何，结果相似，但会导致效能改变。
用GPC分析的结局之间的相关性导致Δ及其估计值发生实质性但可预测的改变。在进行临床试验样本量估计时应考虑相关性。
