Methods: We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions selleck kinase inhibitor with over-and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to
the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability
integral transform(PIT), and by using proper scoring rules (e.g. the logarithmic score).
Results: The instruments under study provide excellent tools for preparing decisions within the SAP in a transparent way when structuring the primary analysis, sensitivity or ancillary analyses, and specific analyses for secondary endpoints. The GKT137831 order mean logarithmic score and DIC discriminate well between different model scenarios. It becomes obvious that the naive choice of a conventional random effects Poisson model is often inappropriate for real-life
count data. The findings are used to specify an appropriate mixed model employed in the sensitivity analyses of an ongoing phase III trial.
Conclusions: The proposed Bayesian methods are not only appealing for buy BYL719 inference but notably provide a sophisticated insight into different aspects of model performance, such as forecast verification or calibration checks, and can be applied within the model selection process. The mean of the logarithmic score is a robust tool for model ranking and is not sensitive to sample size. Therefore, these Bayesian model selection techniques offer helpful decision support for shaping sensitivity and ancillary analyses in a statistical analysis plan of a clinical trial with longitudinal count data as the primary endpoint.”
“The dysfunction of pancreatic beta-cell and the reduction in beta-cell mass are the decisive events in the progression of type 2 diabetes. There is increasing evidence that cytokines play important roles in the procedure of beta-cell failure. Cytokines, such as IL-1 beta, IFN-gamma, TNF-alpha, leptin, resistin, adiponectin, and visfatin, have been shown to diversely regulate pancreatic beta-cell function. Recently, islet-derived cytokine PANcreatic DERived factor (PANDER or FAM3B) has also been demonstrated to be a regulator of islet beta-cell function.