Kim Soyoung, Martens Michael J, Ahn Kwang Woo
Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Biom J. 2025 Feb;67(1):e70021. doi: 10.1002/bimj.70021.
In biomedical studies, investigators often encounter clustered data. The cluster sizes are said to be informative if the outcome depends on the cluster size. Ignoring informative cluster sizes in the analysis leads to biased parameter estimation in marginal and mixed-effect regression models. Several methods to analyze data with informative cluster sizes have been proposed; however, methods to test the informativeness of the cluster sizes are limited, particularly for the marginal model. In this paper, we propose a score test and a Wald test to examine the informativeness of the cluster sizes for a generalized linear model, a Cox model, and a proportional subdistribution hazards model. Statistical inference can be conducted through weighted estimating equations. The simulation results show that both tests control Type I error rates well, but the score test has higher power than the Wald test for right-censored data while the power of the Wald test is generally higher than the score test for the binary outcome. We apply the Wald and score tests to hematopoietic cell transplant data and compare regression analysis results with/without adjusting for informative cluster sizes.
在生物医学研究中，研究者经常会遇到聚类数据。如果结果取决于聚类大小，那么就称聚类大小是有信息价值的。在分析中忽略有信息价值的聚类大小会导致边际回归模型和混合效应回归模型中的参数估计出现偏差。已经提出了几种分析具有有信息价值聚类大小的数据的方法；然而，检验聚类大小信息价值的方法有限，特别是对于边际模型。在本文中，我们提出了一种得分检验和一种 Wald 检验，用于检验广义线性模型、Cox 模型和比例子分布风险模型中聚类大小的信息价值。可以通过加权估计方程进行统计推断。模拟结果表明，两种检验都能很好地控制第一类错误率，但对于右删失数据，得分检验的功效高于 Wald 检验，而对于二元结局，Wald 检验的功效通常高于得分检验。我们将 Wald 检验和得分检验应用于造血细胞移植数据，并比较了调整和未调整有信息价值聚类大小情况下的回归分析结果。
