预期延迟效应下生存分析中的可视化假设检验

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-05-06 DOI:10.1002/pst.2393
José L Jiménez, Isobel Barrott, Francesca Gasperoni, Dominic Magirr
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引用次数: 0

摘要

当我们预计延迟效应会导致非比例危险时,对于采用时间到事件终点的随机临床试验(RCT)的主要分析,什么才是适当的统计方法?这个问题最近引起了很多争论。标准方法是对数秩检验和/或 Cox 比例危险度模型。统计文献中也探讨了其他方法,如加权对数秩检验和基于限制平均生存时间(RMST)的检验。虽然与标准对数秩检验相比,加权对数秩检验可以获得较高的检验功率,但在特定条件下,某些权重的选择可能会导致I型误差膨胀。此外,加权对数秩检验与数学上明确的总结性指标并无关联。另一方面,基于 RMST 的检验统计允许研究两条生存曲线在预先指定的时间点 τ $$ \tau $$ 前的平均差异--这是一个数学上明确的总结性指标。然而,由于强调τ $$ \tau $$之前的差异,这种检验统计可能无法完全反映新疗法在长期生存方面的益处。在本文中,我们介绍了一种直接比较加权对数秩检验和基于 RMST 检验的图形方法。从这一新角度出发,我们可以更明智地选择分析方法,而不仅仅局限于功率和 I 型误差的比较。
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Visualizing hypothesis tests in survival analysis under anticipated delayed effects.

What can be considered an appropriate statistical method for the primary analysis of a randomized clinical trial (RCT) with a time-to-event endpoint when we anticipate non-proportional hazards owing to a delayed effect? This question has been the subject of much recent debate. The standard approach is a log-rank test and/or a Cox proportional hazards model. Alternative methods have been explored in the statistical literature, such as weighted log-rank tests and tests based on the Restricted Mean Survival Time (RMST). While weighted log-rank tests can achieve high power compared to the standard log-rank test, some choices of weights may lead to type-I error inflation under particular conditions. In addition, they are not linked to a mathematically unambiguous summary measure. Test statistics based on the RMST, on the other hand, allow one to investigate the average difference between two survival curves up to a pre-specified time point τ $$ \tau $$ -a mathematically unambiguous summary measure. However, by emphasizing differences prior to τ $$ \tau $$ , such test statistics may not fully capture the benefit of a new treatment in terms of long-term survival. In this article, we introduce a graphical approach for direct comparison of weighted log-rank tests and tests based on the RMST. This new perspective allows a more informed choice of the analysis method, going beyond power and type I error comparison.

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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
Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes. Prediction Intervals for Overdispersed Poisson Data and Their Application in Medical and Pre-Clinical Quality Control. Treatment Effect Measures Under Nonproportional Hazards. Bayesian Response Adaptive Randomization for Randomized Clinical Trials With Continuous Outcomes: The Role of Covariate Adjustment. PKBOIN-12: A Bayesian Optimal Interval Phase I/II Design Incorporating Pharmacokinetics Outcomes to Find the Optimal Biological Dose.
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