13: Calculating Correlation in Observational Studies when only One Variable Contains Repeated Measures

Music City Center 

Correlations of variables with multiple measurements often arise in human observational studies. In such studies, variables of interest may include household level measurements as well as participant level measurements. Correlations between participant data and household data are difficult to calculate due to the imbalance of available samples across variables.
While several approaches have been developed to estimate correlation when both variables contain repeated measures, less work has explored the scenario where only one variable contains repeated measures and not both. This work evaluates several correlation approaches for this scenario to compare the approaches developed for repeated measures data. Using simulated data, comparisons are made across the following correlation approaches: Pearson's, subject level averaging, regression models, and mixed effects models with compound symmetry covariance matrix. Several simulated scenarios are considered, including varying the underlying true correlation values and different noise levels. Mean Squared Error (MSE), confidence interval width and coverage probabilities are used to assess the methods.

correlation

repeated measures

observational data 

Section on Statistics in Epidemiology