在精密瘤学临床试验中,基准测量无进展生存率作为主要终点
Federico Nichetti1,2, Jennifer Hüllein3, Pauline du Rusquec4,5
1Oncology Unit 1, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy. federico.nichetti@iov.veneto.it.
NPJ precision oncology
|December 15, 2025
概括
基于Kernel的Kaplan Meier分析被推用于评估无进展生存率 (PFSratio) 的精密瘤学试验. 这种方法准确地处理审查,并避免分布假设,与其他分析不同.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
背景情况:
- 无进展生存率 (PFSratio) 是精密瘤学 (PO) 中的一个关键终点.
- 对PFSratio进行准确的统计分析对于评估试验性治疗非常重要.
- 现有的PFSratio分析方法有局限性,特别是对于被审查的数据.
研究的目的:
- 基于PFSratio的试验分析的五种方法进行基准测试.
- 评估PFSratio试验设计的两个方法.
- 确定在PO中进行PFSratio分析的最强大和最可靠的方法.
主要方法:
- 基于计数,卡普兰·梅尔,基于内核的卡普兰·梅尔,参数和中级分析方法的基准分析.
- 对GBVE和韦布尔试验设计方法的评估.
- 来自五项临床试验 (>800名患者) 的数据分析.
主要成果:
- 基于内核的Kaplan Meier分析是推的,因为它能够在没有分布假设的情况下处理信息审查.
- 对五项临床试验的分析显示,PFS1/PFS2相关性较弱 (τ范围为0.17-0.35).
- 基于内核的分析提供了无偏的中位数SPFSratio(δ=1.3) =33%,超过了其他方法的>10%审查.
结论:
- 基于Kernel的Kaplan Meier方法是PO中PFSratio分析最推的方法.
- 试验设计方法在具有高PFS1/PFS2相关性和中位数比率的情况下表现最好.
- 该PROPHETS R包和Shiny应用程序实现了这种推的方法.
相关概念视频
Cancer Survival Analysis
629
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
629
Kaplan-Meier Approach
536
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
536
Comparing the Survival Analysis of Two or More Groups
533
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
533
Targeted Cancer Therapies
8.6K
The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
There are several types of targeted therapies against...
8.6K


