在R (DESCIPHR) 中用于癌症干预和人口健康的离散事件模拟模型:一个开源的管道
Selina Pi1, Carolyn M Rutter2, Carlos Pineda-Antunez3
1Department of Biomedical Data Science, School of Medicine, Stanford University, Palo Alto, CA.
medRxiv : the preprint server for health sciences
|June 4, 2025
概括
这项研究介绍了DESCIPHR,这是使用离散事件模拟 (DES) 和贝叶斯校准进行癌症建模的开源框架. 它通过评估干预措施和改进癌症护理策略来帮助卫生政策决策.
科学领域:
- 决策科学 决策科学 决策科学
- 卫生政策 卫生政策
- 计算生物学 计算生物学
背景情况:
- 模拟模型对于卫生政策决策至关重要,特别是对于癌症,这是全球主要的死亡原因.
- 离散事件模拟 (DES) 和贝叶斯校准是模拟复杂健康状况和处理数据不确定性的强大工具.
研究的目的:
- 为癌症进展的DES模型结构,通过贝叶斯校准进行参数估计和政策评估提供端到端的指导.
- 引入DESCIPHR,一个开源框架,集成DES,贝叶斯校准和查策略评估用于癌症干预.
主要方法:
- 开发了DESCIPHR,这是一个开源框架,具有癌症自然史的灵活DES模型.
- 实现了参数估计的贝叶斯校准,包括用于数据告知先前分布的自动化方法.
- 利用神经网络模拟器来提高贝叶斯校准准确度和灵活性.
主要成果:
- DESCIPHR框架为癌症建模和干预评估提供了一个结构化的方法.
- 自动预先分配生成和神经网络模拟器提高了贝叶斯校准的效率和准确性.
- 代码库促进了各种数据源的整合,以提供强有力的卫生政策建议.
结论:
- DESCIPHR提供了一个可适应的模板,用于在癌症研究中构建决策模型.
- 该框架支持对癌症干预风险和益处的评估,帮助决策.
- 这项工作解决了在癌症健康政策中应用DES和贝叶斯校准的全面指导的需要.
相关概念视频
01:21Cancer Survival Analysis
329
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...
329
01:18Introduction To Survival Analysis
188
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...
188
01:15Assumptions of Survival Analysis
101
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.
101
01:12Statistical Software for Data Analysis and Clinical Trials
505
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
505
01:20Comparing the Survival Analysis of Two or More Groups
156
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...
156
01:09Censoring Survival Data
69
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...
69