Search

Purpose: The aim of this study was to evaluate the effect of ordered subsets maximization expectation (OSEM) and filtered backprojection (FBP) reconstruction algorithms for the qualitative interpretation of DAT-SPECT. Methods: Data were acquired using an anthropomorphic striatal phantom and a gamma camera (SPECT/CT). Images were reconstructed using different values of the covariates applied in the OSEM and FBP algorithms (iteration, subsets, order, and cut-off frequency). Image quality was compared according to each set of covariates used in the reconstruction and extracted quantitative parameters of the image quality, such as C, CV RCR, and RSR, as well as the angular coefficients of the edges in regions of interest. Results: Variations in the order from 0 to 10 do not influence the image if a trustworthy cut-off frequency range for FBP (0.9 to 1.6) and OSEM (1.2 to 1.5) are used. For OSEM, the iteration value and the number of subsets that guarantee better quality were those suggested by the developer of the algorithm used (3i8s). Conclusion: For a quality image, it is necessary to use the range of values found for the covariables order and cut-off frequency of the Butterworth filter in the FBP and OSEM reconstruction. OSEM reconstruction shows superiority in the images compared with FBP when the recommended three iterations and eight subsets are used. In the quantitative analyses, the evaluation showed that increasing the number of updates while allowing a better distinction of certain areas, culminates with a degradation image, making it inappropriate for clinical.

This paper presents estimates for the parameters included in long-term mixture and non-mixture lifetime models, applied to analyze survival data when some individuals may never experience the event of interest. We consider the case where the lifetime data have a three-parameter Burr XII distribution, which includes the popular Weibull mixture model as a special case. Classical and Bayesian procedures are used to get point estimates and confidence intervals for the unknown parameters. We consider a general survival model where the scale and shape parameters of the Burr XII distribution are dependent of some covariates. To illustrate the proposed methodology, we consider an application considering a leukaemia data set where the proposed model gives better fit for the data when compared to other existing models.

AbstractIn this paper, we introduce a Bayesian analysis for bivariate geometric distributions applied to lifetime data in the presence of covariates and censored data using Markov Chain Monte Carlo (MCMC) methods. We show that the use of a discrete bivariate geometric distribution could bring us some computational advantages when compared to standard existing bivariate exponential lifetime distributions introduced in the literature assuming continuous lifetime data as for example, the exponential Block and Basu bivariate distribution. Posterior summaries of interest are obtained using the popular OpenBUGS software. A numerical illustration is introduced considering a medical data set related to the recurrence times of infection for kidney patients.

To characterize a motivational profile of reasons for smoking among teenagers. To investigate the influence of clinical and social elements on observed scores.

Accelerated life testing of an item under more severe than normal conditions is commonly used by industrial statisticians to reduce test time and costs. We consider, from the Bayesian point of view, the problem of accelerated iife tests when the parameter of interest is estimated via the orthogonal prameters (Cox and Reid, 1987) and a type II censoring mechanism for exponential survival data. One important advantage of this orthogonality is to obtain a closed form for the posterior distribution and its mode under the Jeffrey's prior for the parameters involved in the model. The method is illustrated by an example

In this paper, we present some considerations on inferences based on the use of asymptotical normality of the maximum likelihood estimators of the bivariate exponential distribution (BVED) of Block and Basu (1974) considering accelerated life tests with a power rule model. We show in one numerical example, the effect of a different parametrization to improve the accuracy of the obtained inferences on the parameters of the model. We also consider Bayesian methods to get inferences on the parameters of the BVED applied to accelerated life tests.

Assuming a Weibull distribution, the posterior distribution for the median survival time is derived in the presence of arbitrary right censorship. In the design of clinical triaLs, suppose k follow-up periods have been completed and it is desired to plan the follow-up period k+1. In this context, criteria are presented that can be employed in determining the number of new patients to be enrolled in the follow-up period k+1.

A Bayesian approach to the problem of a constant hazard with a single change-point is developed using noninformative reference priors. We also present a generalization for the comparison for two treatments.

This paper reports a broad study using epidemic-related counting data of COVID-19 disease caused by the novel coronavirus (SARS-CoV-2). The considered dataset refers to 119 countries’ daily counts of reported cases and deaths in a fixed period. For the data analysis, it has been adopted a beta regression model assuming different regions of the world where it was possible to discover important economic, health and social factors affecting the behavior of the pandemic in different countries. The Bayesian method was applied to fit the proposed model. Some interesting conclusions were obtained in this study, which could be of great interest to epidemiologists, health authorities, and the general public in the face of the forthcoming hard times of the global pandemic.