Sample size and power calculations for correlations between bivariate longitudinal data.
The analysis of a baseline predictor with a longitudinally measured outcome is well established and sample size calculations are reasonably well understood. Analysis of bivariate longitudinally measured outcomes is gaining in popularity and methods to address design issues are required. The focus in a random effects model for bivariate longitudinal outcomes is on the correlations that arise between the random effects and between the bivariate residuals. In the bivariate random effects model, we estimate the asymptotic variances of the correlations and we propose power calculations for testing and estimating the correlations. We compare asymptotic variance estimates to variance estimates obtained from simulation studies and compare our proposed power calculations for correlations on bivariate longitudinal data to power calculations for correlations on cross-sectional data.
Comulada
W Scott
WS
UCLA Center for Community Health, 10920 Wilshire Blvd Suite 350, Los Angeles, CA 90024-6543, USA. scomulad@ucla.edu
Weiss
Robert E
RE
D000465:Algorithms; D015982:Bias; D015928:Cognitive Behavioral Therapy; D003198:Computer Simulation; D018849:Controlled Clinical Trials as Topic; D018562:Disease Transmission, Infectious / Q000517:prevention & control; D015658:HIV Infections / Q000517:prevention & control / Q000635:transmission; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies / Q000379:methods* / Q000706:statistics & numerical data*; D015233:Models, Statistical; D018401:Sample Size; D012725:Sexual Behavior / Q000523:psychology
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