超越脆弱性指数。

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-11-21 DOI:10.1002/pst.2452
Piero Quatto, Enrico Ripamonti, Donata Marasini
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引用次数: 0

摘要

随机临床试验(RCT)的结果经常使用脆性指数(FI)进行评估。虽然脆性指数提供的信息可以补充 p 值的不足,但这一指标存在固有的弱点和缺陷。在本文中,我们将在一个更广泛的框架内建立脆性分析,使其能够可靠地补充 p 值提供的信息。这一视角被命名为强度分析。我们首先提出了一种新的强度指数(SI),可在正态分布环境中采用。该指标既可用于显著性分析,也可用于非显著性分析,而且计算简便,因此与 FI 相比,从阈值的存在开始,就具有令人信服的优势。我们还讨论了时间到事件结果的情况。然后,除了 p 值之外,我们还从 Royall 的统计证据观点出发,使用似然比对强度进行了分析。我们提供了一个新的 R 软件包来进行强度计算,并开展了一项模拟研究来探索 SI 和基于似然比的指标在不同环境下的经验行为。新提出的强度分析被应用于评估最近三项涉及 COVID-19 治疗的试验结果。
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Beyond the Fragility Index.

The results of randomized clinical trials (RCTs) are frequently assessed with the fragility index (FI). Although the information provided by FI may supplement the p value, this indicator presents intrinsic weaknesses and shortcomings. In this article, we establish an analysis of fragility within a broader framework so that it can reliably complement the information provided by the p value. This perspective is named the analysis of strength. We first propose a new strength index (SI), which can be adopted in normal distribution settings. This measure can be obtained for both significance and nonsignificance and is straightforward to calculate, thus presenting compelling advantages over FI, starting from the presence of a threshold. The case of time-to-event outcomes is also addressed. Then, beyond the p value, we develop the analysis of strength using likelihood ratios from Royall's statistical evidence viewpoint. A new R package is provided for performing strength calculations, and a simulation study is conducted to explore the behavior of SI and the likelihood-based indicator empirically across different settings. The newly proposed analysis of strength is applied in the assessment of the results of three recent trials involving the treatment of COVID-19.

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来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
期刊最新文献
Beyond the Fragility Index. A Model-Based Trial Design With a Randomization Scheme Considering Pharmacokinetics Exposure for Dose Optimization in Oncology. Potential Bias Models With Bayesian Shrinkage Priors for Dynamic Borrowing of Multiple Historical Control Data. Subgroup Identification Based on Quantitative Objectives. A Bayesian Dynamic Model-Based Adaptive Design for Oncology Dose Optimization in Phase I/II Clinical Trials.
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