sleev: 一个半参数概率估计的R包,其中包含变量错误
Jiangmei Xiong1, Sarah C Lotspeich2, Joey B Sherrill3
1Department of Biostatistics, Vanderbilt University Medical Center, USA.
Journal of open source software
|February 23, 2026
概括
本研究介绍了用于分析具有测量错误的生物医学数据的R包套. 它有效地实现了子最大概率估计器 (SMLE) 对于具有易出错结果或共变量的两阶段研究.
科学领域:
- 生物医学研究的研究.
- 统计方法学的统计方法.
- 数据科学是数据科学.
背景情况:
- 在生物医学研究中常规收集的数据往往包含结果或共变量的测量错误.
- 两阶段研究设计是常见的,其中仅验证了数据的子样本.
- 分析容易出错的数据需要专门的统计方法.
研究的目的:
- 解决对计算效率高和用户友好的工具的需求,用于分析易出错的数据在两阶段研究中.
- 引入 R 套件,用于实施的最大概率估计器 (SMLE).
- 为了促进基于半参数概率的推断,对易出错的二进制和连续结果和共变量进行推断.
主要方法:
- 使用了子最大概率估计器 (SMLE) 方法.
- 开发了R包,用于实施SMLE进行两阶段研究.
- 该包处理易出错的二进制和连续结果和共变量.
主要成果:
- 该R套件提供了一个用户友好的工具,用于应用SMLE.
- 能够对容易出错的复杂数据进行高效,可靠的分析.
- 支持对具有测量误差的二进制和连续结果进行分析.
结论:
- 实验组合有效填补了在两阶段研究中分析易出错的数据的缺口.
- 它提高了在生物医学研究中使用SMLE的可访问性和效率.
- 该工具支持广泛的数据类型,包括易出错的响应和共变量.
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