Random effects’ distribution assumption on joint mixed modelling

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2018
Özdemir, Celal Oğuz
Joint mixed model is an appealing approach in medical research where it is critical to estimate the odds of a fatal complication that occurs to a patient given the covariate profile such as a risk factor observed over time. For this kind of estimation, joint mixed model is used. In the standard Bayesian analysis of the model, the error variance and random effects’ variance-covariance matrix are apriori modeled independently with Inverse-Gamma and Inverse-Wishart distributions respectively. Recently however, it is shown that joint apriori modeling via Generalized Multivariate Log-Gamma (G-MVLG) distribution is more efficient than the standard Bayesian analysis for these variance components. Our current aim is to inverstigate the robustness of G-MVLG based and standard analysis to random effects’ distributions. Bivariate Gamma, Bivariate Skew-Normal, Normal distribution and their mixture distributions were considered for the true distribution of random effects. Results show that the G-MVLG approach is robust to the underlying true distribution of random effects when the sample size is sufficiently large. For small samples, a robust approach. Simulations and real data study show that DPP for the random effects distributions is less biased and more efficient.
Citation Formats
C. O. Özdemir, “Random effects’ distribution assumption on joint mixed modelling,” M.S. - Master of Science, Middle East Technical University, 2018.