回顾性瘤学的生存数据质量 出版物 出版物
K Goel1, E E Laseinde2, M F Gensheimer2
1Perelman School of Medicine, USA.
概括
大多数癌症研究使用质量不确定的生存数据来源. 虽然随着时间的推移有所改善,但研究人员需要更好的报告和更高质量的数据来准确地确定癌症生存终点.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 生存终点在癌症研究中至关重要.
- 准确性依赖于高质量的数据来源,但电子医疗记录 (EMR) 往往提供不完整的数据.
- 在回顾性瘤学研究中,报告和生存数据源的质量在很大程度上是未知的.
研究的目的:
- 在回顾性癌症研究中评估报告和生存数据源的质量.
- 识别随着时间的推移,数据源质量的趋势.
- 检查数据质量与期刊影响因子或子专业之间的关联.
主要方法:
- 2001年,2011年和2021年在九个瘤学期刊上发表的514项回顾性研究的系统审查.
- 生存终点和结果数据源的提取.
- 数据来源的分类为高质量,不确定的质量或未知.
主要成果:
- 总体存活率是最常见的终点 (80%).
- 14%的研究没有报告其生存数据来源.
- 随着时间的推移,研究越来越多地使用高质量的来源 (2001/2011年13% vs. 2021年26%),但质量不确定的来源仍然更为普遍.
- 放射性瘤学研究不太可能使用高质量的数据来源.
结论:
- 大多数回顾性癌症研究依赖于质量不确定的生存数据来源.
- 尽管有所改善,但仍需要更好地遵守报告准则,并鼓励使用高质量的数据源.
- 期刊影响因子与使用高质量数据源的研究比例没有相关性.
更多相关视频
相关概念视频
Cancer Survival Analysis
458
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...
458
Assumptions of Survival Analysis
200
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.
200
Kaplan-Meier Approach
281
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,...
281
Comparing the Survival Analysis of Two or More Groups
303
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...
303
Introduction To Survival Analysis
409
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...
409
Actuarial Approach
140
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,...
140


