混合治愈半参数加速失效时间模型与部分间隔审查数据
Isabel Li1,2, Jun Ma2, Benoit Liquet2,3
1Brain and Mind Centre, The University of Sydney, Sydney, Australia.
Biometrical journal. Biometrische Zeitschrift
|November 7, 2024
概括
本研究介绍了混合治愈半参数加速失效时间 (AFT) 模型的惩罚性概率方法. 该方法有效地处理部分间隔审查的数据,并且与现有方法相比显示偏差较小.
科学领域:
- 生物统计学 生物统计学
- 生存分析的分析.
- 统计建模 统计建模
背景情况:
- 治愈模型针对的是从未经历过事件的部分人群.
- 加速失效时间 (AFT) 模型适用于当生存时间呈现加速或减速时.
- 混合疗法 AFT模型将这些概念结合起来,以获得复杂的生存数据.
研究的目的:
- 开发一种惩罚性概率方法来估计混合物治疗半参数 AFT 模型.
- 为了适应部分间隔审查的生存数据,包括事件,左,右和间隔审查的时间.
- 为可靠的统计推理提供非对称属性.
主要方法:
- 对于基线危险,使用了惩罚性概率方法与高斯基数函数.
- 整合了一个惩罚函数,以便顺利估计基线危险.
- 在生存数据中允许各种审查类型.
主要成果:
- 建议的惩罚性概率方法显示了可接受的性能.
- 该方法比 smcure R 包更少的偏差,特别是在可识别性问题上.
- 一个关于黑色素瘤复发的现实世界案例研究说明了该方法的适用性.
结论:
- 处罚概率方法为混合治疗半参数 AFT 模型提供了一种可行的方法.
- 开发的方法为复杂的生存数据提供了可靠的估计和推断.
- R包"aftQnp"可用于实际实施.
相关概念视频
Censoring Survival Data
65
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...
65
Kaplan-Meier Approach
102
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,...
102
Comparing the Survival Analysis of Two or More Groups
155
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...
155
Assumptions of Survival Analysis
97
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.
97
Hazard Rate
90
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
90
Introduction To Survival Analysis
184
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...
184


