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Estimating correlation between multivariate longitudinal data in the presence of heterogeneity
Feng Gao1,2, J Philip Miller3, Chengjie Xiong3
1Department of Surgery, Division of Public Health Sciences, Washington University School of Medicine, 660 S. Euclid Ave., St. Louis, MO, 63110, USA. feng@wustl.edu.
BMC Medical Research Methodology
|August 19, 2017
Summary
Researchers explored correlations in longitudinal data using conditional correlation. This method revealed that heterogeneity, not overall trends, drives associations between mean deviation and visual acuity in eye studies.
Area of Science:
- Biostatistics
- Ophthalmology
- Epidemiology
Background:
- Estimating outcome correlations is crucial in clinical and epidemiological research.
- Multivariate longitudinal data allow for assessing the joint evolution of outcomes.
- Bivariate linear mixed models (BLMMs) are used for correlation assessment but assume population homogeneity.
Purpose of the Study:
- To investigate strategies for understanding correlations in multivariate longitudinal data, accounting for potential population heterogeneity.
- To assess the impact of heterogeneity on correlation estimates.
- To apply conditional correlation to analyze longitudinal data in the Ocular Hypertension Treatment Study (OHTS).
Main Methods:
- Utilized longitudinal mean deviation (MD) and visual acuity (VA) data from the OHTS.
- Calculated conditional correlation (marginal correlation given random effects) to describe within-subpopulation associations over time.
- Employed simulation studies to assess the influence of heterogeneity on correlation.
Main Results:
- Significant positive correlations were found between random intercepts (ρ=0.278) and random slopes (ρ=0.579) for longitudinal MD and VA.
- The strength of correlation between MD and VA increased over time.
- Conditional correlation and simulations indicated that heterogeneity, driven by a small subset of participants with rapidly deteriorating MD, primarily induced the observed correlation.
Conclusions:
- Conditional correlation, given random effects, offers a robust method for estimating correlations in multivariate longitudinal data with unobserved heterogeneity.
- This approach enhances the understanding of outcome associations in potentially non-homogenous populations.
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