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疫苗试验中保护相关性评估的半参数拟似然和拟得分。

Semiparametric pseudo-score and pseudo-likelihood for evaluating correlate of protection in vaccine trials.

机构信息

Novartis Pharmaceuticals, East Hanover, New Jersey, USA.

Takeda Pharmaceuticals, Cambridge, Massachusetts, USA.

出版信息

Stat Med. 2023 Aug 30;42(19):3317-3332. doi: 10.1002/sim.9807. Epub 2023 May 29.

Abstract

In vaccine clinical trials, vaccine efficacy endpoint analysis is usually associated with in high cost or extended study duration, due to the generally low infection rate. Correlate of protection (CoP), which refers to surrogate endpoint, usually immunological response, that can reliably predict the treatment effect, provides a more efficient and less costly approach to evaluate the vaccine. To handle the challenge of the missingness in the unobserved surrogate immune biomarker, the pseudo-score (PS) method, semiparametric method and pseudo-likelihood (PL) method demonstrated their advantages on different aspects. In this article, we propose new methodologies to combine the advantages of PS and PL with semiparametric methods respectively, to achieve higher estimate efficiency, allow continuous baseline predictor variable, and handle multiple surrogate markers. The advantage of our methodologies are demonstrated by a simulation study in different settings and applied to a case study, which eventually can improve the chance of a successful trial.

摘要

在疫苗临床试验中,由于感染率通常较低,疫苗功效终点分析通常与高成本或延长研究时间相关。保护相关因素(CoP),即替代终点,通常是指能够可靠地预测治疗效果的免疫学反应,为评估疫苗提供了一种更有效和成本更低的方法。为了处理未观察到的替代免疫生物标志物缺失的问题,伪评分(PS)方法、半参数方法和伪似然(PL)方法在不同方面展示了其优势。在本文中,我们提出了新的方法学,分别将 PS 和 PL 的优势与半参数方法相结合,以实现更高的估计效率,允许连续的基线预测变量,并处理多个替代标志物。我们的方法学通过在不同设置下的模拟研究和应用于案例研究来证明其优势,最终可以提高试验成功的机会。

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