A review of applications of the Bayes factor in psychological research.

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Whole-brain radiation therapy without a thermoplastic mask.

The aim of the study was to investigate the clinical feasibility of whole-brain radiation therapy without a thermoplastic mask. Positioning and intra-fractional motion monitoring were performed using optical surface scanning. The motion threshold was 3 mm/3 degrees. The group mean vector deviation was 1.1 mm. The roll was larger compared to pitch and rotation. Two patients out of 30 were not able to lie still. All other patients completed their treatment successfully without a mask. With a probability of success of 93%, we concluded that irradiation without a mask is a clinically feasible method.

Occurrence and mechanism of visual phosphenes in external photon beam radiation therapy and how to influence them.

BACKGROUND AND PURPOSE: Two plausible mechanisms to explain the appearance of visual phosphenes are: direct activation of the photochemicals in the retina and the generation of Cherenkov radiation in the vitreous humour. In this clinical trial we investigated the occurrence of visual phosphenes in external photon beam radiation therapy. MATERIAL AND METHODS: Logistic regression analysis is used to examine whether seeing light flashes and seeing steady light depended on the ambient light intensity and the dose. RESULTS: In total, 465 treatments of 25 patients were analysed. The odds of seeing light flashes multiply by 0,926 as the ambient light intensity increases by 10 lux. Similarly, the odds multiply by 1,604 as the dose to the retina increases by 10 cGy. The odds of seeing steady light multiply by 1,540 as the dose to the vitreous humour increases by 10 cGy. CONCLUSIONS: We postulate that one should reduce the dose rate, instruct patients to keep the eyes open and increase the illuminance in the treatment room to reduce the probability of experiencing visual phosphenes. We hypothesize that melanopsin is involved in the visual phosphenes and that fatigue of patients might be correlated with the observation of visual phosphenes.

Automatic Bayes Factors for Testing Equality- and Inequality-Constrained Hypotheses on Variances.

In comparing characteristics of independent populations, researchers frequently expect a certain structure of the population variances. These expectations can be formulated as hypotheses with equality and/or inequality constraints on the variances. In this article, we consider the Bayes factor for testing such (in)equality-constrained hypotheses on variances. Application of Bayes factors requires specification of a prior under every hypothesis to be tested. However, specifying subjective priors for variances based on prior information is a difficult task. We therefore consider so-called automatic or default Bayes factors. These methods avoid the need for the user to specify priors by using information from the sample data. We present three automatic Bayes factors for testing variances. The first is a Bayes factor with equal priors on all variances, where the priors are specified automatically using a small share of the information in the sample data. The second is the fractional Bayes factor, where a fraction of the likelihood is used for automatic prior specification. The third is an adjustment of the fractional Bayes factor such that the parsimony of inequality-constrained hypotheses is properly taken into account. The Bayes factors are evaluated by investigating different properties such as information consistency and large sample consistency. Based on this evaluation, it is concluded that the adjusted fractional Bayes factor is generally recommendable for testing equality- and inequality-constrained hypotheses on variances.

Bayesian evaluation of constrained hypotheses on variances of multiple independent groups.

Research has shown that independent groups often differ not only in their means, but also in their variances. Comparing and testing variances is therefore of crucial importance to understand the effect of a grouping variable on an outcome variable. Researchers may have specific expectations concerning the relations between the variances of multiple groups. Such expectations can be translated into hypotheses with inequality and/or equality constraints on the group variances. Currently, however, no methods are available for testing (in)equality constrained hypotheses on variances. This article proposes a novel Bayesian approach to this challenging testing problem. Our approach has the following useful properties: First, it can be used to simultaneously test multiple (non)nested hypotheses with equality as well as inequality constraints on the variances. Second, our approach is fully automatic in the sense that no subjective prior specification is needed. Only the hypotheses need to be provided. Third, a user-friendly software application is included that can be used to perform this Bayesian test in an easy manner. (PsycINFO Database Record