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Random effects’ distribution assumption on joint mixed modelling
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Date
2018
Author
Özdemir, Celal Oğuz
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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.
Subject Keywords
Medical statistics.
,
Random variables.
,
Distribution (Probability theory).
,
Bayesian statistical decision theory.
URI
http://etd.lib.metu.edu.tr/upload/12622651/index.pdf
https://hdl.handle.net/11511/27627
Collections
Graduate School of Natural and Applied Sciences, Thesis
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C. O. Özdemir, “Random effects’ distribution assumption on joint mixed modelling,” M.S. - Master of Science, Middle East Technical University, 2018.