Confidence limits for the averted infections ratio estimated via the counterfactual placebo incidence rate.
Confidence limits for the averted infections ratio estimated via the counterfactual placebo incidence rate.
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DOI：
10.1515/scid-2021-0002
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发表时间：
2021-01-01
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避免感染率（AIR）是一种新型指标，用于量化具有至事件时间结局的活性对照非劣效性临床试验中的效果保留。在主要制剂中，AIR要求估计反事实安慰剂的发生率。我们描述了两种方法来计算置信限的空气给定的点估计这个参数，一个封闭形式的解决方案的基础上的泰勒级数展开（三角洲方法）和迭代方法的基础上的配置文件的可能性。对于每种方法，在（1）AIR的真实值（2）反事实事件的预期数量（3）活性对照治疗的有效性的网格值上计算置信下限和置信上限的精确覆盖概率。重点关注置信下限，它决定了是否可以宣布非劣效性，通过delta方法实现的覆盖范围小于或大于标称覆盖范围，这取决于AIR的真实值。相比之下，由轮廓似然方法实现的覆盖率是一致准确的。轮廓似然法是首选，因为更好的覆盖性能，但更简单的三角洲的方法是有效的，当实验治疗是不低于控制治疗的有效性。一个互补的贝叶斯方法，可以应用时，反事实的发病率可以表示为先验分布，也概述了。
The averted infections ratio (AIR) is a novel measure for quantifying the preservation-of-effect in active-control non-inferiority clinical trials with a time-to-event outcome. In the main formulation, the AIR requires an estimate of the counterfactual placebo incidence rate. We describe two approaches for calculating confidence limits for the AIR given a point estimate of this parameter, a closed-form solution based on a Taylor series expansion (delta method) and an iterative method based on the profile-likelihood. For each approach, exact coverage probabilities for the lower and upper confidence limits were computed over a grid of values of (1) the true value of the AIR (2) the expected number of counterfactual events (3) the effectiveness of the active-control treatment. Focussing on the lower confidence limit, which determines whether non-inferiority can be declared, the coverage achieved by the delta method is either less than or greater than the nominal coverage, depending on the true value of the AIR. In contrast, the coverage achieved by the profile-likelihood method is consistently accurate. The profile-likelihood method is preferred because of better coverage properties, but the simpler delta method is valid when the experimental treatment is no less effective than the control treatment. A complementary Bayesian approach, which can be applied when the counterfactual incidence rate can be represented as a prior distribution, is also outlined.