使用加速失效时间模型估计时间变化的治疗切换效应,并应用于血液透析的血管接入
Fang-I Chu1,2, Yuedong Wang2
1Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, US.
概括
从中央静脉导管 (CVC) 到动脉静脉 (AVF) 或移植 (AVG) 的血液透析血管接入的切换有利于患者. 这种转换的积极影响随着时间的推移而变化,较早的变化会产生更好的结果.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 生物统计学 生物统计学
- 血管外科 血管外科
背景情况:
- 血管接入对于血液透析患者至关重要.
- 中枢静脉导管 (CVC) 与动脉静脉 (AVF) 和动脉静脉移植 (AVG) 相比,与较差的结果有关.
- 切换血管通道的时间动态和时间依赖性仍然不完全理解.
研究的目的:
- 为了研究切换血液透析血管接入对患者结果的影响如何随着时间的推移而变化.
- 为了确定从CVC切换到AVF/AVG的时间是否会影响患者的治疗结果.
主要方法:
- 使用加速失效时间 (AFT) 模型,具有时间变化的效果.
- 模拟了非参数的访问切换的时间依赖效应,使用立方线.
- 相关观察到的生存时间 (无变化) 和反事实时间 (访问切换) 来估计切换效应.
主要成果:
- 从CVC切换到AVG的好处取决于切换的时间.
- 早些时候从CVC切换到AVG表明对结果有更大的积极影响.
- AFT模型有效地考虑了基线和时间变化的协变量效应.
结论:
- 血管接入修改的时间显著影响血液透析患者的结果.
- 建议立即从CVC切换到AVG,以提高生存效益.
- 时间变化的效应模型提供了一个灵活的框架来分析干预措施随时间推移的影响.
相关概念视频
Introduction To Survival Analysis
277
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...
277
Assumptions of Survival Analysis
154
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.
154
Comparing the Survival Analysis of Two or More Groups
222
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...
222
Kaplan-Meier Approach
179
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,...
179
Actuarial Approach
96
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,...
96
Hemodialysis I: Introduction
54
Hemodialysis (HD) is a medical treatment that artificially removes waste products, excess fluids, and toxins from the blood when the kidneys are no longer able to perform these functions effectively. In this process, blood is filtered through a semipermeable membrane, allowing for the selective removal of waste while preserving necessary components like blood cells and proteins. Hemodialysis is typically performed in patients with end-stage renal disease (ESRD) or severe kidney...
54


