Abstract

Repeated data are increasingly collected in studies to investigate the trajectory of change in measurements over time. Determining a link between one repeated measurement with another that is considered as the biomarker for disease progression, may provide a new target for drug development. When a third variable is associated with one of the two measurements, partial correlation after eliminating the effect of that variable is able to provide reliable estimate for association as compared to the existing raw correlation for repeated data. We propose using linear regression models to compute residuals by modeling a relationship between each measurement and a third variable. The computed residuals are then used in a linear mixed model (implemented by SAS Proc Mixed) to compute partial correlation for repeated data. Alternatively, the partial correlation may be computed as the average of partial correlations at each visit. We provide two real examples to illustrate the application of the proposed partial correlation and conduct extensive numerical studies to evaluate the proposed partial correlation coefficients.

摘要

研究中越来越多地收集重复数据，以调查测量值随时间变化的轨迹。确定一项重复测量与另一项被视为疾病进展生物标志物之间的联系，可能为药物开发提供新的目标。当第三个变量与两个测量值之一相关时，与重复数据的现有原始相关性相比，消除该变量的影响后的部分相关能够提供可靠的关联估计。我们建议使用线性回归模型通过对每个测量值与第三个变量之间的关系进行建模来计算残差。然后将计算出的残差用于线性混合模型（由 SAS Proc Mixed 实现）来计算重复数据的部分相关性。或者，可以将部分相关性计算为每次访问的部分相关性的平均值。我们提供了两个真实的例子来说明所提出的偏相关的应用，并进行广泛的数值研究来评估所提出的偏相关系数。