基于几何平均危险比的混合模型样本量计算及其在非比例危险中的应用。

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-05-01 Epub Date: 2023-12-28 DOI:10.1002/pst.2353
Zixing Wang, Qingyang Zhang, Allen Xue, James Whitmore
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引用次数: 0

摘要

随着癌症免疫疗法的出现,在生存分析中经常会观察到一些特殊现象,包括延迟治疗效果、治愈率、治疗效果递减和交叉生存。它们违反了比例危险模型假设,给传统的试验设计和分析策略带来了独特的挑战。许多方法(如治愈率模型)都是基于混合模型开发的,以纳入这些特征。在这项工作中,我们扩展了混合模型,以处理多种非比例模式,并开发了几何平均危险比(gAHR)来量化治疗效果。我们还根据对数秩检验的非中心性参数进一步推导出样本大小和功率公式,并对每个参数对性能的影响进行了深入分析。模拟研究表明,在不同的非比例危险情况下,我们的新方法比基于比例危险的计算方法具有明显优势。此外,两项真实试验的混合建模演示了如何在实践中使用不同生物标记物和早期疗效结果患者生存分布的先验信息。与基于模拟的设计相比,新方法提供了一种更有效的方法来计算功率和样本量,并具有较高的估计精度。总之,理论推导和实证研究都证明了所提出的方法有望为未来的创新试验设计提供支持。
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Sample size calculation for mixture model based on geometric average hazard ratio and its applications to nonproportional hazard.

With the advent of cancer immunotherapy, some special features including delayed treatment effect, cure rate, diminishing treatment effect and crossing survival are often observed in survival analysis. They violate the proportional hazard model assumption and pose a unique challenge for the conventional trial design and analysis strategies. Many methods like cure rate model have been developed based on mixture model to incorporate some of these features. In this work, we extend the mixture model to deal with multiple non-proportional patterns and develop its geometric average hazard ratio (gAHR) to quantify the treatment effect. We further derive a sample size and power formula based on the non-centrality parameter of the log-rank test and conduct a thorough analysis of the impact of each parameter on performance. Simulation studies showed a clear advantage of our new method over the proportional hazard based calculation across different non-proportional hazard scenarios. Moreover, the mixture modeling of two real trials demonstrates how to use the prior information on the survival distribution among patients with different biomarker and early efficacy results in practice. By comparison with a simulation-based design, the new method provided a more efficient way to compute the power and sample size with high accuracy of estimation. Overall, both theoretical derivation and empirical studies demonstrate the promise of the proposed method in powering future innovative trial designs.

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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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