de Leon, Alexander R.Roy, Mili2016-01-072016-01-072016-01-072015Roy, M. (2016). Conditional Dependence in Joint Modelling of Longitudinal Non-Gaussian Outcomes (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25412http://hdl.handle.net/11023/2730The thesis is motivated by the limitations of conventional joint modelling strategies based on linear and generalized linear mixed models (LMMs/GLMMs). The class of so-called Gaussian copula mixed models (GCMMs), introduced by Wu and de Leon (2014) to generalize conventional LMMs/GLMMs to non-Gaussian settings, was adopted, and simulations were conducted to investigate the impact of incorrectly ignoring the conditional dependence between outcomes, given the random effects, on the performance of maximum likelihood estimates (MLEs). A variety of scenarios involving shared or correlated random effects were considered, and implementation of the correct and misspecified joint models was done in SAS’s PROC NLMIXED. Although MLEs of fixed effects were only slightly impacted by the conditional independence misspecification, MLEs based on the correct GCMM yielded generally better performances than those from the incorrect model. Data on pediatric pain (Weiss, 2005; Withanage et al., 2015) were used for illustration.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.StatisticsGCMMGLMMmaster thesis10.11575/PRISM/25412