多状态生存过程的联合建模与信息化检查方案:适用于糖尿病进展的应用
Yuxi Zhu1,2,3, Joshua J Joseph4, Neena Thomas5
1Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA. Yuxi.Zhu@uhhospitals.org.
BMC medical research methodology
|April 16, 2025
概括
这项研究开发了2型糖尿病 (T2D) 进展的联合模型,揭示了社会经济劣势影响医疗保健访问,黑人患者面临更高的T2D风险. 该模型考虑了非随机的患者检查时间表.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 多状态生存模型 (MSM) 在临床研究中很常见,特别是在2型糖尿病 (T2D) 等疾病中.
- 传统的MSM经常认为患者检查时间表与疾病进展无关,这是不现实的.
- 获得医疗保健的频率可能会受到疾病控制和治疗的影响,影响生存分析.
研究的目的:
- 为分析T2D进展以及患者检查频率开发一个联合统计模型.
- 为了解释疾病状态过渡和医疗保健访问模式之间的依赖.
- 确定影响T2D进展和医疗保健利用的风险因素.
主要方法:
- 为T2D进展和信息化考试计划构建了一个四州联合模型.
- 使用逻辑线性模型来评估年龄,性别,种族和社会经济劣势对T2D过渡强度和访问频率的影响.
- 预期最大化 (EM) 算法用于概率框架内的参数估计.
主要成果:
- 在社会经济上处于劣势地区的个人在所有T2D状态中显示出更低的医疗保健访问频率.
- 与非西班牙裔白人患者相比,黑人患者从正常发展到糖尿病前期,T2D和不受控制的T2D的风险更高.
- 该模型成功地将疾病进展动态与医疗保健获取模式相结合.
结论:
- 开发的联合模型为分析生存数据提供了一个强大的框架,在这种情况下,检查过程取决于疾病状态.
- 这种方法提供了比传统的MSM更全面的理解,通过捕捉医疗保健访问频率的变化.
- 调查结果突出了基于社会经济地位和种族/种族的医疗保健准入和疾病进展风险的差异.
相关概念视频
01:18Introduction To Survival Analysis
119
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...
119
01:20Comparing the Survival Analysis of Two or More Groups
87
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...
87
01:15Assumptions of Survival Analysis
60
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.
60
01:20Actuarial Approach
39
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,...
39
01:19Survival Tree
39
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
39
01:24Kaplan-Meier Approach
55
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,...
55