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BMC Med Res Methodol. 2017 Aug 17;17(1):124. doi: 10.1186/s12874-017-0398-1.

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity.

Author information

1
Department of Surgery, Division of Public Health Sciences, Washington University School of Medicine, 660 S. Euclid Ave., St. Louis, MO, 63110, USA. feng@wustl.edu.
2
Division of Biostatistics, Washington University School of Medicine, 660 S. Euclid Ave., St. Louis, MO, 63110, USA. feng@wustl.edu.
3
Division of Biostatistics, Washington University School of Medicine, 660 S. Euclid Ave., St. Louis, MO, 63110, USA.
4
Department of Surgery, Division of Public Health Sciences, Washington University School of Medicine, 660 S. Euclid Ave., St. Louis, MO, 63110, USA.
5
Department of Ophthalmology & Visual Sciences, Washington University School of Medicine, 660 S. Euclid Ave., St. Louis, MO, 63110, USA.

Abstract

BACKGROUND:

Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average.

METHODS:

Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data.

RESULTS:

There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples.

CONCLUSION:

Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

KEYWORDS:

Bivariate linear mixed model (BLMM); Correlation; Heterogeneity; Multivariate longitudinal data

PMID:
28818061
PMCID:
PMC5561646
DOI:
10.1186/s12874-017-0398-1
[Indexed for MEDLINE]
Free PMC Article

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