闪亮的事件:协调纵向数据用于现实世界生存率估计
Alyssa Obermayer1, Joshua Davis1, Divya Priyanka Talada1
1H. Lee Moffitt Cancer Center and Research Institute.
Research square
|August 13, 2025
概括
ShinyEvents是一个新的网络工具,用于分析随时间推移的患者治疗数据. 它通过可视化临床事件和执行生存分析,帮助研究人员了解不同的治疗方法如何影响患者的结果.
科学领域:
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
- 临床数据分析 临床数据分析
背景情况:
- 纵向数据分析对于了解患者治疗结果至关重要.
- 现有的工具很难有效地整合复杂,多层次的时间序列临床数据.
- 需要先进的框架来分析患者的旅程和治疗的有效性.
研究的目的:
- 开发ShinyEvents,这是一个基于Web的框架,用于分析复杂的纵向患者数据.
- 为了实现临床事件和治疗途径的交互可视化和队列级分析.
- 为了促进现实世界无进展生存 (rwPFS) 分析,并将治疗线与结果关联起来.
主要方法:
- 开发了ShinyEvents,这是一个用户友好的,基于Web的平台,用于纵向数据集成.
- 实施了针对个体患者临床事件的交互时间线生成.
- 启用队列级分析,包括治疗聚类,终点定义,桑基和游泳者图,以及生存分析 (Kaplan-Meier,Cox回归).
主要成果:
- 闪亮的事件成功地集成了多层次的纵向患者数据.
- 该框架为治疗线和临床课程提供交互式可视化 (Sankey,Swimmer图).
- 该工具可实现实时生存分析,包括rwPFS,并将治疗策略与结果关联起来.
结论:
- ShinyEvents为医疗保健中复杂的纵向数据分析提供了全面的解决方案.
- 该框架有助于更深入地了解治疗有效性和患者的治疗结果.
- ShinyEvents支持实时集成和分析多层患者旅程数据.
相关概念视频
01:15Assumptions of Survival Analysis
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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01:18Introduction To Survival Analysis
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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
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01:20Comparing the Survival Analysis of Two or More Groups
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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...
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01:20Actuarial Approach
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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
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01:24Kaplan-Meier Approach
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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,...
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01:09Censoring Survival Data
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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