通过反事实安慰剂发生率估计的避免感染率的置信限。

David T Dunn, Oliver T Stirrup, David V Glidden
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引用次数: 1

摘要

目的:避免感染比率(AIR)是一种量化主动对照非劣效性临床试验中具有事件发生时间结局的效果保存的新措施。在主要公式中,AIR要求对安慰剂的反事实发生率进行估计。我们描述了在给定该参数的点估计的情况下计算AIR置信限的两种方法,一种是基于泰勒级数展开(delta方法)的封闭形式解,另一种是基于剖面似然的迭代方法。方法: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)主动控制治疗的有效性的值的网格上计算下限和上限的准确覆盖概率。结果:关注下限,这决定了是否可以声明非劣效性,delta方法实现的覆盖率要么小于要么大于名义覆盖率,这取决于AIR的真实值。相比之下,由轮廓似然方法获得的覆盖率始终是准确的。结论:轮廓似然法具有更好的覆盖性能,是优选的方法,而当实验处理的效果不低于对照处理时,更简单的delta法是有效的。还概述了一种补充贝叶斯方法,当反事实发生率可以表示为先验分布时,可以应用该方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Confidence limits for the averted infections ratio estimated via the counterfactual placebo incidence rate.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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