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考克斯模型推断相对危险和纯风险从分层重量校准的案例-队列数据
Lola Etievant1, Mitchell H Gail2
1Division of Cancer Epidemiology and Genetics, Biostatistics Branch, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD, 20850-9780, USA. lola.etievant@nih.gov.
Lifetime data analysis
|April 2, 2024
概括
本研究引入了统一的R软件,用于高效的案例和队列研究分析,增强Cox模型估计和纯风险计算. 该软件支持各种设计选项,促进更广泛地采用原则统计方法.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 统计软件开发 统计软件开发
背景情况:
- 案例-队列研究提供了有效的数据收集,通过仅在案例和随机子队列上收集完整的共变量数据.
- 像分层和重量校准等先进的分析技术可以提高考克斯模型估计和纯风险计算的效率.
- 用户友好的软件的有限可用性阻碍了在医学研究中采用这些先进的病例队列分析方法.
研究的目的:
- 提出统一的统计方法和配套的R软件 (CaseCohortCoxSurvival) 用于分析案例队列研究.
- 为了促进各种设计和分析选项的实施,包括分层和重量校准.
- 为估计考克斯模型参数,累积基线危险和共变量特定纯风险提供可访问的工具.
主要方法:
- 开发了一个R包,CaseCohortCoxSurvival,实现一个统一的分析框架.
- 利用适用于双相采样设计的影响函数.
- 集成的差异计算,考虑复杂的采样,包括分层,重量校准和缺失的数据.
- 澄清了对病例-队列数据的"强大"差异估计器的适当使用.
主要成果:
- 在R软件,CaseCohortCoxSurvival,允许分析案例和队列研究与或没有分层和/或重量校准.
- 该软件处理具有或没有替换的子队列采样,并适应分层设计中缺少的第二阶段数据.
- 在Cox比例危险模型中为日志相对危险,累积基线危险和纯风险提供了强大的统计推断.
结论:
- 开发的R软件和统一的方法简化和促进使用高效和有原则的方法来分析案例和队列研究.
- 这项工作解决了用于高级案例群组设计的现有软件的缺口.
- 鼓励更广泛地应用复杂的统计技术,以改善流行病学研究.
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