通过使用标准参数模型和灵活参数分支模型,在所有原因和相对生存框架内比较生存外推,使用瑞典癌症注册表
Enoch Yi-Tung Chen1, Yuliya Leontyeva1, Chia-Ni Lin2
1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
概括
推用于10年生存超分的分线模型,而相对生存框架,特别是分线模型,对于癌症患者的终身生存估计是最好的.
科学领域:
- 卫生技术评估 卫生技术评估
- 生存分析的分析.
- 癌症流行病学 癌症流行病学
背景情况:
- 卫生技术评估通常使用参数模型进行推断来评估受限制的平均生存时间和预期寿命.
- 在相对存活框架中的分支模型显示出对估计癌症患者预期寿命的希望.
- 需要对具有全因生存率的支线模型和具有相对生存框架的参数模型进行进一步研究.
研究的目的:
- 为了比较标准参数模型和spline模型的生存外推精度.
- 在相对生存和全因生存框架内评估模型.
- 评估10年生存率和终身生存率外推的性能.
主要方法:
- 利用瑞典癌症登记处 (1981-1990) 的数据,对5种癌症类型进行了追踪,直到2020年.
- 根据癌症和年龄组 (18-99岁) 将患者分为15个队列.
- 在所有原因和相对生存框架内安装了6个标准参数和3个支线模型,与Kaplan-Meier估计进行了比较.
主要成果:
- 分裂模型在10年生存预测方面通常表现优于标准参数模型.
- 在所有原因和相对生存框架之间没有观察到显著的差异,以10年超值计算.
- 相对生存框架,特别是与支线模型的相对生存框架,显示出与观察到的数据更好的一致性,用于终身生存外推.
结论:
- 建议用于10年生存率外推的线条模型.
- 建议使用相对存活框架,特别是与spline模型,用于终身存活外推.
- 突出了潜在的过高/低估,使用全因相对相对生存框架来进行终身推断.
相关概念视频
Comparing the Survival Analysis of Two or More Groups
188
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...
188
Cancer Survival Analysis
348
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...
348
Survival Curves
159
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
159
Assumptions of Survival Analysis
128
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.
128
Parametric Survival Analysis: Weibull and Exponential Methods
436
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
436
Introduction To Survival Analysis
239
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...
239


