在非小细胞肺癌的无残疾预期寿命的估计,基于真实世界的数据
Shin-Mao Lin1, Szu-Chun Yang2, Tzu-I Wu1
1Department of Environmental and Occupational Medicine, National Cheng Kung University Hospital, College of Medicine, Tainan, Taiwan.
Scientific reports
|August 16, 2023
概括
这项研究量化了非小细胞肺癌 (NSCLC) 患者残疾的社会影响. 先进的NSCLC阶段显著减少了无残疾预期寿命 (DFLE),突出了早期诊断和长期护理考虑的必要性.
科学领域:
- 在瘤学瘤学.
- 公共卫生 公共卫生
- 卫生经济学 卫生经济学
背景情况:
- 非小细胞肺癌 (NSCLC) 显著影响患者的生活质量和社会成本.
- 量化残疾及其对预期寿命的影响对于全面的医疗技术评估至关重要.
研究的目的:
- 在NSCLC患者中估计无残疾预期寿命 (DFLE) 和DFLE损失.
- 探索DFLE,质量调整后预期寿命 (QALE) 和它们各自的损失之间的关联.
- 为社会提供关于NSCLC相关残疾的长期负担的观点.
主要方法:
- 相互关联的国家数据库和滚动算法来估计NSCLC患者的终身存活功能.
- 使用EuroQOL-5维度 (EQ-5D) 和Barthel指数 (BI) 进行反复的生活质量和残疾评估 (2011-2020年).
- 采用线性混合模型来估计实用性和残疾分数,将BI分数 ≤70 分类为需要长期护理.
主要成果:
- 根据阶段和年龄,DFLE的差异很大:15.3岁 (50-64岁,I-IIIa阶段) 和1.2岁 (65-89岁,IIIb-IV阶段).
- 较晚的阶段 (例如,50-64岁的20.7岁,IIIb-IV阶段) 的DFLE损失更高,这表明效应比年龄更强.
- 晚期幸存者报告说,在大多数功能领域需要更多的帮助;由于不适和抑郁症,QALE比DFLE更短.
结论:
- 晚期NSCLC阶段是减少DFLE的主要驱动因素,比年龄更大.
- 社会卫生技术评估必须纳入NSCLC中功能残疾的终身持续时间.
- 建议早期诊断NSCLC,以潜在地减轻长期的社会护理负担.
相关概念视频
Kaplan-Meier Approach
180
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,...
180
Cancer Survival Analysis
384
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...
384
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
Statistical Methods for Analyzing Epidemiological Data
411
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
411
Life Tables
130
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
130
Assumptions of Survival Analysis
155
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.
155


