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Advisor(s)
Abstract(s)
In health related research it is common to have multiple outcomes of interest in a single study. These outcomes are often analysed separately, ignoring the correlation between them. One would expect that a multivariate approach would be a more efficient alternative to individual analyses of each outcome. Surprisingly, this is not always the case. In this article we discuss different settings of linear models and compare the multivariate and univariate approaches. We show that for linear regression models, the estimates of the regression parameters associated with covariates that are shared across the outcomes are the same for the multivariate and univariate models while for outcome-specific covariates the multivariate model performs better in terms of efficiency.
Description
Keywords
Ordinary least squares Correlated errors Generalized least squares Monte Carlo
Citation
Oliveira, R., & Pinto, A. T. (2015). Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression? Journal of Biometrics & Biostatistics, 1–6. https://www.hilarispublisher.com/open-access/analyzing-multiple-outcomes-is-it-really-worth-the-use-ofmultivariate-linear-regression-2155-6180-1000256.pdf