D'Angelo Gina M, Luo Jingqin, Xiong Chengjie
Division of Biostatistics, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO 63110, USA.
J Biom Biostat. 2012 Dec;3(8). doi: 10.4172/2155-6180.1000155.
In the dementia area it is often of interest to study relationships among regional brain measures; however, it is often necessary to adjust for covariates. Partial correlations are frequently used to correlate two variables while adjusting for other variables. Complete case analysis is typically the analysis of choice for partial correlations with missing data. However, complete case analysis will lead to biased and inefficient results when the data are missing at random. We have extended the partial correlation coefficient in the presence of missing data using the expectation-maximization (EM) algorithm, and compared it with a multiple imputation method and complete case analysis using simulation studies. The EM approach performed the best of all methods with multiple imputation performing almost as well. These methods were illustrated with regional imaging data from an Alzheimer's disease study.
在痴呆症领域，研究大脑区域测量值之间的关系常常很有意义；然而，通常有必要对协变量进行调整。偏相关经常用于在调整其他变量的同时关联两个变量。对于存在缺失数据的偏相关分析，完全病例分析通常是首选的分析方法。然而，当数据随机缺失时，完全病例分析会导致有偏差且效率低下的结果。我们使用期望最大化（EM）算法在存在缺失数据的情况下扩展了偏相关系数，并通过模拟研究将其与多重填补方法和完全病例分析进行了比较。在所有方法中，EM方法表现最佳，多重填补方法的表现几乎与之相同。这些方法通过一项阿尔茨海默病研究的区域成像数据进行了说明。
