Joint modeling of clustered binary data with crossed random effects via the Gaussian copula mixed model
| dc.contributor.advisor | de Leon, Alexander R. | |
| dc.contributor.advisor | Wu, Jingjing | |
| dc.contributor.author | Jaman, Ajmery | |
| dc.contributor.committeemember | Bingrui, Cindy Sun | |
| dc.contributor.committeemember | Ngamkham, Thuntida | |
| dc.date | 2019-11 | |
| dc.date.accessioned | 2019-07-12T21:07:47Z | |
| dc.date.available | 2019-07-12T21:07:47Z | |
| dc.date.issued | 2019-07-11 | |
| dc.description.abstract | Models with crossed random effects are common in reader-based diagnostic studies, where the same group of readers evaluate patients for certain diseases; an example is diabetic retinopathy study in Alberta, Canada. Although generalized linear mixed models (GLMMs) are well developed for non-Gaussian responses (e.g., binary outcomes) with crossed random effects, evaluation of the marginal likelihood is still technically and computationally demanding and can become prohibitive in applications, since the data cannot be grouped into independent blocks. The available estimation methods are also not free from problems. A recent approach involves application of data cloning (DC) to obtain maximum likelihood (ML) estimates using a Bayesian framework. Their approach is proved to be superior over the other two alternatives they considered in terms of providing relatively unbiased and efficient parameter estimates. However, this approach is based on a multivariate latent Gaussian description of the multiple correlated binary outcomes. In this thesis, we relax this assumption by allowing for disparate non-Gaussian latent variables for the binary responses, and propose a joint modeling via the Gaussian copula mixed model (GCMM). We applied maximum pairwise likelihood (PL) estimation instead of doing full ML analysis to reduce computational complexities. We conducted simulation studies with a setting analogous to the diabetic retinopathy data to see the performance of PL estimators for GCMM with crossed random effects. Simulation results suggest that although the estimation of regression coefficients and correlation parameter exhibit no problem, a much bigger sample size is required for the other scale parameters to provide reasonably accurate approximate results. We also analyzed the retinopathy data with the proposed approach considering three different conditional margins. | en_US |
| dc.identifier.citation | Jaman, A. (2019). Joint modeling of clustered binary data with crossed random effects via the Gaussian copula mixed model (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/36741 | |
| dc.identifier.uri | http://hdl.handle.net/1880/110626 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Science | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University 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. | en_US |
| dc.subject | retinopathy | en_US |
| dc.subject | gaussian copula | en_US |
| dc.subject | crossed random effects | en_US |
| dc.subject.classification | Statistics | en_US |
| dc.title | Joint modeling of clustered binary data with crossed random effects via the Gaussian copula mixed model | en_US |
| dc.type | master thesis | en_US |
| thesis.degree.discipline | Mathematics & Statistics | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Master of Science (MSc) | en_US |
| ucalgary.item.requestcopy | true | en_US |