Partial Correlation Coefficient for a Study With Repeated Measurements

Document Type

Article

Publication Date

7-20-2020

Publication Title

Statistics in Biopharmaceutical Research

First page number:

1

Last page number:

8

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 regressionmodels 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.

Keywords

Alzheimer’s Disease; Parkinson’s Disease; Partial Correction; Proc Mixed; Repeated Measurements

Disciplines

Medicine and Health Sciences | Public Health

Language

English

UNLV article access

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