纵向数据中的各种缺失归算方法:模拟研究和真实数据分析
Mina Jahangiri1, Anoshirvan Kazemnejad2, Keith S Goldfeld3
1Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
BMC medical research methodology
|July 6, 2023
概括
这项研究评估了缺少纵向数据的归算方法. 纵向回归树算法,特别是单次归算轨迹平均值 (SI平均值) 方法,显示出比参数模型更高的性能.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 缺少数据是纵向数据分析的一个重大挑战.
- 为了解决缺失的数据,存在各种单次计量 (SI) 和多次计量 (MI) 方法.
- 在纵向研究中,非参数计量方法的有效性需要进一步研究.
研究的目的:
- 调查纵向回归树算法的性能,作为在纵向研究中赋值缺失数据的非参数方法.
- 使用模拟和现实数据,比较27种不同的归算方法 (SI和MI) 的有效性.
- 使用诸如平均平方误差 (MSE),根-平均平方误差 (RMSE) 和中位数绝对偏差 (MAD) 等指标来评估归算性能.
主要方法:
- 对比了各种各样的归算方法,包括交叉,轨迹平均值,插值,复制平均值和MI.
- 利用模拟数据场景和来自德黑兰心脏代谢遗传研究 (TCGS) 的真实数据,共3645名参与者.
- 使用预测变量 (如年龄,性别和BMI) 建模的静缩和放缩血压 (SBP/DBP),采用参数和非参数纵向模型.
主要成果:
- 纵向回归树算法在基于MSE,RMSE和MAD标准的线性混合效应模型 (LMM) 上表现优越,用于TCGS和模拟数据,在一个失踪随机 (MAR) 机制下模拟数据.
- 在适应非参数模型时,27种归算方法的性能在很大程度上相似.
- 与其他归算方法相比,单次归算轨迹平均值 (SI 平均值) 方法显示出更好的性能.
结论:
- SI和MI的归算方法在纵向回归树算法中表现得更好,而不是在参数纵向模型中.
- 建议研究人员使用轨迹平均值 (traj-mean) 方法在纵向数据中赋值缺失值.
- 选择最佳的归算方法取决于特定的模型和感兴趣的数据结构.
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