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Seminar on Tuesday, October 10 at 2 pm
October 10, 2017 @ 2:00 pm - 3:00 pm
Title: Conditional dependence in joint modelling of longitudinal non-Gaussian outcomes
Speaker: Mili Roy, MSc
Research Assistant
Werklund School of Education
University of Calgary, Canada
Abstract: The study 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 in-
vestigate 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 misspeci ed joint models was done in SAS’s PROC NLMIXED. Although MLEs of
fixed effects were only slightly impacted by the conditional independence misspeci cation, 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.
