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对于连续结果的累积概率模型的非对称属性
Chun Li1, Yuqi Tian2, Donglin Zeng3
1Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90033, USA.
概括
累积概率模型 (CPM) 为连续结果提供灵活的分析. 本研究通过修改数据来确定CPM的非对称性质,确保可靠的回归系数估计和转换函数准确性.
科学领域:
- 生物统计学 生物统计学
- 统计建模 统计建模
- 流行病学 流行病学
背景情况:
- 连续结果回归通常需要结果转换,通常是预先指定的或来自参数家族的.
- 累积概率模型 (CPM) 通过将连续结果视为有序类别来处理一个非参数的方法,提供灵活性.
- 对于标准的CPM来说,建立非对称性属性是具有挑战性的,因为转换的无限性质.
研究的目的:
- 确定用于连续结果的累积概率模型 (CPM) 的非对称性属性.
- 为了证明估计回归系数和转换函数的均一致性和非对称分布.
- 确认估计的回归系数达到半参数效率极限.
主要方法:
- 修改了连续结果数据,设置了边界,并将这些边界之外的结果视为两个不同的顺序类别.
- 将累积概率模型 (CPM) 应用于此修改的数据集.
- 已证明估计回归系数和转换函数在定义边界内的统一一致性.
- 为这些估计得出了共同的非对称分布.
- 进行模拟,将修改后的CPM方法与标准CPM方法进行比较.
- 重新分析了HIV阳性患者的真实世界数据集.
主要成果:
- 对于估计的回归系数和在指定的范围内转换函数的统一一致性被证明.
- 描述了估计的联合非对称分布.
- 估计的回归系数被证明可以达到半参数效率边界.
- 模拟表明,修改后的CPM方法在仅修改一小部分数据时,会产生与标准CPM非常相似的结果.
结论:
- 修改后的累积概率模型 (CPM) 方法成功地为连续结果分析建立了非对称的属性.
- 该方法提供了回归系数和转换函数的一致且高效的估计.
- 这种方法很强大,即使有轻微的数据修改,也与标准CPM差异很小,并且适用于真实世界的数据.
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