For analyzing such responses, this paper presents a pragmatic multivariate mixed-effects model. The model can flexibly accommodate both symmetric and asymmetric structures by utilizing a novel multivariate transformed distribution belonging to the family of elliptical distributions. It also offers a ...
A common approach for dealing with longitudinal categorical responses is to use the Generalized Linear Mixed Model (GLMM). This model induces the potential relation between response variables over time via a vector of random effects, assumed to be shared parameters in the non-ignorable missing ...
Mixed models with set-specific random effects are a flexible tool to model the different sets of items jointly. However, computational problems typically arise as the number of sets increases. This is especially true when the random-effects distribution cannot be integrated out analytically, as with...
As such, we introduce the multivariate-spatio-temporal mixed effects model (MSTM) to analyze areal data with multivariate-spatio-temporal dependencies. The proposed MSTM extends the notion of Moran's I basis functions to the multivariate-spatio-temporal setting. This extension leads to several ...
Such a model is known as a multivariate linear mixed-effects regression model. Conceptually, the random-effects represent the collective effect of unmeasured variables that contribute to the difference in the observed responses from subject to subject, above and beyond those effects associated with ...
Multiphase modelingBivariate mixed effects modelCompeting risksCause-specific hazardsFrailty modelsJoint modelingIn many clinical studies that involve follow-up, it is common to observe one or more sequences of longitudinal measurements, as well as one or more time to event outcomes. A competing risks...
A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented concentrates ...
meta‐regressionmultivariate random effects modelsThis tutorial on advanced statistical methods for meta-analysis can be seen as a sequel to the recent Tutorial in Biostatistics on meta-analysis by Normand, which focused on elementary methods. Within the framework of the general linear mixed model ...
A joint model with random effects for longitudinal mixed ordinal and continuous responses, with potentially non-random missing values in both types of responses is proposed. The presented model simultaneously considers a multivariate probit regression model for the missing mechanisms, which provides the ...
This study presents discussion on the effects of correlation among response respect to estimator properties in mixed logit model on multivariate binary response. It is assumed that each respondent was observed for T response. YSUBit/SUB is the tSUPth/SUP response for the iSUPth/SUP individual/...