Fitting spatial models with a Gaussian random field as spatial random effect poses computational challenges for Markov Chain Monte Carlo (MCMC) methods, primarily due to two factors: computational speed and convergence of chains for the hyperparameters. To deal with this, a Gaussian random field can be approximated by a Gaussian Markov random field using stochastic partial differential equations. This methodology is commonly used in “latent Gaussian models”, where the inference is done by the Integrated Nested Laplace Approximations, but rarely used in an MCMC method. In this contribution, we evaluated different parameterizations of the approximated Gaussian random field, specifically using the Hamiltonian Monte Carlo algorithm of the Stan software. A simulation study demonstrated that models using the hyperparameters ρ and σu were better able to estimate the values used to simulate the spatial random field. Their speed computation were faster compared to models parameterized with κ and τ. In real data application, the index of relative abundance estimated for Pollock indicates similar trends for the six models proposed. However, models incorporating ρ and σu demonstrated faster computation compared to those utilizing κ and τ, corroborating the results found in the simulation. Even more important, none of these models encountered convergence issues, as indicated by the Rhat statistic.

A estatística bayesiana tem sido cada vez mais utilizada em ensaios clínicos, oferecendo maior flexibilidade e eficiência no desenvolvimento de novos fármacos.

Neste seminário abordaremos este tópico utilizando como exemplo base num grande ensaio clínico muito conhecido mas que poucos sabem que utilizou métodos bayesianos. Vamos explorar em detalhe a metodologia utilizada no ensaio e em como é aplicável a outros ensaios. Será também abordado o tema de escolha do tipo de distribuições a priori e como escolher parâmetros de uma distribuição.

Misdiagnosis can occur when different case definitions are used by clinicians (relative misdiagnosis) or when failing the genuine diagnosis of another disease (misdiagnosis in a strict sense). In complex diseases, such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), this problem translates to a recurrent difficulty in reproducing research findings. To explore these effects, we simulated data from case-control studies under the assumption of misdiagnosis in a strict sense. We estimated the power to detect a genuine association between a potential causal factor and ME/CFS and demonstrated how current research studies may have suboptimal power. To address the implications of these findings, suggestions for how power can be improved are given and explained within the context of the disease.

This seminar will begin with an introduction of the multidimensional construct of healthcare access, providing a well-established definition and common objectives in access measurement and inference. Different approaches will be presented, focusing on rigorous mathematical models to estimate access, including optimization and simulation under uncertainty of the model inputs. Important aspects will be covered including spatial dependence in the decision parameters of optimization models used to estimate healthcare access and Bayesian hierarchical models used to specify the sampling distributions of model inputs. The models will be illustrated for modeling access to mental healthcare in Georgia, United States.