Chakraborty Hrishikesh, Helms Ronald W, Sen Pranab K, Cohen Myron S
Quintiles Inc., Research Triangle Park, NC, USA.
Stat Med. 2003 May 15;22(9):1457-64. doi: 10.1002/sim.1505.
Estimating the correlation coefficient between two outcome variables is one of the most important aspects of epidemiological and clinical research. A simple Pearson's correlation coefficient method is usually employed when there are complete independent data points for both outcome variables. However, researchers often deal with correlated observations in a longitudinal setting with missing values where a simple Pearson's correlation coefficient method cannot be used. General linear mixed models (GLMM) techniques were used to estimate correlation coefficients in a longitudinal data set with missing values. A random regression mixed model with unstructured covariance matrix was employed to estimate correlation coefficients between concentrations of HIV-1 RNA in blood and seminal plasma. The effects of CD4 count and antiretroviral therapy were also examined. We used data sets from three different centres (650 samples from 238 patients) where blood and seminal plasma HIV-1 RNA concentrations were collected from patients; 137 samples from 90 different patients without antiviral therapy and 513 samples from 148 patients receiving therapy were considered for analysis. We found no significant correlation between blood and semen HIV-1 RNA concentration in the absence of antiviral therapy. However, a moderate correlation between blood and semen HIV-1 RNA was observed among subjects with lower CD4 counts receiving therapy. Our findings confirm and extend the idea that the concentrations of HIV-1 in semen often differ from the HIV-1 concentration in blood. Antiretroviral therapy administered to subjects with low CD4 counts result in sufficient concomitant reduction of HIV-1 in blood and semen so as to improve the correlation between these compartments. These results have important implications for studies related to the sexual transmission of HIV, and development of HIV prevention strategies.
估计两个结果变量之间的相关系数是流行病学和临床研究最重要的方面之一。当两个结果变量都有完整的独立数据点时，通常采用简单的皮尔逊相关系数法。然而，研究人员在纵向研究中经常处理存在缺失值的相关观测数据，此时简单的皮尔逊相关系数法无法使用。通用线性混合模型（GLMM）技术被用于估计存在缺失值的纵向数据集中的相关系数。采用具有非结构化协方差矩阵的随机回归混合模型来估计血液和精液中HIV-1 RNA浓度之间的相关系数。同时还研究了CD4细胞计数和抗逆转录病毒疗法的影响。我们使用了来自三个不同中心的数据集（238名患者的650个样本），这些样本是从患者身上采集的血液和精液中HIV-1 RNA浓度数据；分析时考虑了来自90名未接受抗病毒治疗的不同患者的137个样本以及来自148名接受治疗患者的513个样本。我们发现，在未接受抗病毒治疗的情况下，血液和精液中HIV-1 RNA浓度之间没有显著相关性。然而，在接受治疗且CD4细胞计数较低的受试者中，血液和精液中HIV-1 RNA之间存在中度相关性。我们的研究结果证实并扩展了这样一种观点，即精液中HIV-1的浓度通常与血液中HIV-1的浓度不同。对CD4细胞计数较低的受试者进行抗逆转录病毒治疗，可使血液和精液中的HIV-1同时充分减少，从而提高这些部位之间的相关性。这些结果对与HIV性传播相关的研究以及HIV预防策略的制定具有重要意义。
