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Sample size and power calculations for correlations between bivariate longitudinal data.

W Scott Comulada1, Robert E Weiss

  • 1UCLA Center for Community Health, 10920 Wilshire Blvd Suite 350, Los Angeles, CA 90024-6543, USA. scomulad@ucla.edu

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Summary
This summary is machine-generated.

This study introduces methods for analyzing bivariate longitudinal outcomes, focusing on correlations within random effects models. It provides power calculations for estimating these correlations, crucial for study design.

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Standard analysis of single longitudinal outcomes and sample size calculations are established.
  • Bivariate longitudinal outcome analysis is increasingly used, necessitating robust design methods.
  • Random effects models are key for handling correlated longitudinal data.

Purpose of the Study:

  • To develop methods for analyzing bivariate longitudinal outcomes.
  • To focus on correlations between random effects and residuals in a bivariate random effects model.
  • To propose power calculations for testing and estimating these correlations.

Main Methods:

  • Utilized a bivariate random effects model.
  • Estimated asymptotic variances of correlations between random effects and residuals.
  • Developed and proposed power calculations for correlations in bivariate longitudinal data.

Main Results:

  • Asymptotic variance estimates were compared with simulation-based estimates.
  • Proposed power calculations for bivariate longitudinal data were compared to cross-sectional data power calculations.
  • The study provides a framework for sample size determination in bivariate longitudinal studies.

Conclusions:

  • The proposed methods offer a way to estimate and test correlations in bivariate longitudinal data.
  • Power calculations are essential for designing studies with bivariate longitudinal outcomes.
  • This work advances the statistical methodology for complex longitudinal study designs.