线性混合模型在估计使用多种随机效应分布的玻璃眼进展的结构性速率方面的表现
Swarup S Swaminathan1, Samuel I Berchuck2, J Sunil Rao3
1Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida.
Ophthalmology science
|February 6, 2024
概括
使用高斯分布或Student t分布的线性混合模型 (LMM) 准确地估计了使用OCT扫描的玻璃眼患者的结构损失. 这些LMM优于普通最小平方 (OLS) 回归来预测未来视网膜神经纤维层厚度.
科学领域:
- 眼科医生 眼科 眼科
- 生物统计学 生物统计学
- 医疗成像医学成像
背景情况:
- 玻璃眼是全球不可逆转失明的主要原因,其特点是逐渐的结构损伤,特别是视网膜神经纤维层 (RNFL).
- 光学一致性断层扫描 (OCT) 是量化RNFL厚度和监测玻璃眼病进展的关键成像方式.
- 准确估计RNFL损失率对于及时诊断和有效管理眼病至关重要.
研究的目的:
- 用OCT数据比较线性混合模型 (LMMs) 与高斯,Student t和log-gamma随机效应分布的性能,以估计RNFL在青光眼患者的厚度损失率.
- 评估和比较这些LMM与普通最小平方 (OLS) 回归的预测准确度,以估计未来的RNFL厚度.
主要方法:
- 一个回顾性队列研究,利用来自巴斯科姆·帕尔默玻璃眼存储库 (BPGR) 的数据.
- 包括至少有5个可靠的RNFL OCT测试超过2年的眼睛,数据是使用OLS和LMMs模拟的,随机效应分布有所变化.
- 预测建模涉及使用LMMs预测后续RNFL厚度,使用Watanabe-Akaike信息标准 (WAIC) 和模拟数据的平均绝对误差 (MAE) 评估性能.
主要成果:
- 总共分析了5,766只眼睛的35,862个OCT扫描,平均随访时间为7.0年.
- 与OLS相比,LMMs显著降低了预测未来RNFL厚度的平均绝对误差 (P < 0.001).
- 学生t分布模型显示出最好的模型匹配 (最低的WAIC),高斯和学生t模型在预测准确性方面显著超过了逻辑马模型.
结论:
- 线性混合模型 (LMMs) 与传统方法 (如OLS回归) 相比表现优越,用于估计青光眼中RNFL厚度损失率.
- 学生t分布为估计RNFL厚度变化提供了最适合的模型,尽管高斯和学生t分布在准确度上取得了类似的改进.
- LMMs,特别是高斯分布或Student t分布,可以提供更准确和可靠的估计,对玻璃眼的结构损失.
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