Publication: Quasi-complete separation in random effects of binary response mixed models
Quasi-complete separation in random effects of binary response mixed models
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Sauter, R., & Held, L. (2016). Quasi-complete separation in random effects of binary response mixed models. Journal of Statistical Computation and Simulation, 86(14), 2781–2796. https://doi.org/10.1080/00949655.2015.1129539
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Clustered observations such as longitudinal data are often analysed with generalized linear mixed models (GLMM). Approximate Bayesian inference for GLMMs with normally distributed random effects can be done using integrated nested Laplace approximations (INLA), which is in general known to yield accurate results. However, INLA is known to be less accurate for GLMMs with binary response. For longitudinal binary response data it is common that patients do not change their health state during the study period. In this case the grouping c
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- Sauter, Rafael
- Held, Leonhard
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Sauter, R., & Held, L. (2016). Quasi-complete separation in random effects of binary response mixed models. Journal of Statistical Computation and Simulation, 86(14), 2781–2796. https://doi.org/10.1080/00949655.2015.1129539
