Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique-Santé, CNRS, Villeurbanne, France.
Service de Biostatistique et Bioinformatique, Hospices Civils de Lyon, Lyon, France.
Biom J. 2021 Apr;63(4):893-906. doi: 10.1002/bimj.202000001. Epub 2021 Feb 22.
Generalized pairwise comparisons (GPCs) are a statistical method used in randomized clinical trials to simultaneously analyze several prioritized outcomes. This procedure estimates the net benefit (Δ). Δ may be interpreted as the probability for a random patient in the treatment group to have a better overall outcome than a random patient in the control group, minus the probability of the opposite situation. However, the presence of right censoring introduces uninformative pairs that will typically bias the estimate of Δ toward 0. We propose a correction to GPCs that estimates the contribution of each uninformative pair based on the average contribution of the informative pairs. The correction can be applied to the analysis of several prioritized outcomes. We perform a simulation study to evaluate the bias associated with this correction. When only one time-to-event outcome was generated, the corrected estimates were unbiased except in the presence of very heavy censoring. The correction had no effect on the power or type-1 error of the tests based on the Δ. Finally, we illustrate the impact of the correction using data from two randomized trials. The illustrative datasets showed that the correction had limited impact when the proportion of censored observations was around 20% and was most useful when this proportion was close to 70%. Overall, we propose an estimator for the net benefit that is minimally affected by censoring under the assumption that uninformative pairs are exchangeable with informative pairs.
广义成对比较（GPC）是一种在随机临床试验中用于同时分析多个优先结局的统计方法。该方法估计净效益（Δ）。Δ 可以解释为治疗组中随机患者的总体结局优于对照组中随机患者的概率减去相反情况的概率。然而，右删失的存在引入了无信息对，这通常会导致 Δ 的估计偏向 0。我们提出了一种对 GPC 的修正方法，该方法基于有信息对的平均贡献来估计每个无信息对的贡献。该修正可应用于多个优先结局的分析。我们进行了一项模拟研究来评估该修正的偏倚。当仅生成一个生存时间结局时，除了存在非常严重的删失外，校正后的估计值是无偏的。该修正对基于 Δ 的检验的功效或第一类错误没有影响。最后，我们使用来自两项随机试验的数据说明了该修正的影响。说明性数据集表明，当删失观察的比例约为 20%时，该修正的影响有限，而当该比例接近 70%时，该修正的影响最大。总体而言，我们提出了一种在假设无信息对与有信息对可交换的情况下，受删失影响最小的净效益估计量。
