具有故障时间流程的双相设计,受非易受性的约束
Fangya Mao1, Li C Cheung1, Richard J Cook2
1Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Rockville, MD 20852, United States.
Biometrics
|March 6, 2024
概括
本研究引入了高效的两阶段设计,用于具有昂贵的共变量和长期幸存者的流行病学研究. 新的方法改善了复杂的生存数据的资源分配和分析,优于现有的部分采样方案.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 生存分析的分析.
背景情况:
- 双相设计通过在子样本上测量昂贵的共变量来优化流行病学研究中的资源使用.
- 现有的双相设计通常依赖于Cox模型对右侧审查的数据.
- 由于非易感性,处理长期幸存者需要先进的统计建模.
研究的目的:
- 为流行病学研究制定高效的两阶段设计策略,使用长期幸存者的非敏感部分进行流行病学研究.
- 提出回归框架,以适应治疗分数的混合模型.
- 为复杂的生存场景引入新的双变量残留依赖设计.
主要方法:
- 使用的混合物模型包括一个不敏感的分数.
- 考虑了三个回归框架:逻辑治愈模型,易感的比例危险模型和易感性和故障时间的联合模型.
- 开发了一种新类的双变量残留依赖设计,用于同时估计易感性和失效时间.
主要成果:
- 拟议的两阶段设计策略在模拟中显示出与现有的部分采样方案相比更高的效率.
- 这种新的双变量剩余依赖设计有效地解决了在模拟易受性和失效时间方面的挑战.
- 这些方法成功地应用于前列腺,肺部,结肠直肠和卵巢癌查试验的现实数据.
结论:
- 开发的两阶段设计为使用治疗分数的流行病学研究提供了统计学上稳健和资源高效的方法.
- 新的设计策略特别有价值,用于分析复杂的生存数据与非易感人群.
- 这项工作为改善临床和流行病学研究中长期存活数据分析提供了实际工具.
更多相关视频
13:54A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
Published on: August 18, 2023
4.5K
05:03Author Spotlight: Unveiling Mechanisms of Stress Resilience - Significant Findings, Advancements, and Future Research
Published on: December 15, 2023
4.0K
相关概念视频
01:15Assumptions of Survival Analysis
126
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.
126
01:18Introduction To Survival Analysis
235
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...
235
01:20Comparing the Survival Analysis of Two or More Groups
186
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...
186
01:11Hazard Rate
108
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...
108
01:16Crossover Experiments
2.8K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
2.8K
01:09Censoring Survival Data
91
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...
91