Inconsistency identification in network meta-analysis via stochastic search variable selection.

The reliability of the results of network meta-analysis (NMA) lies in the plausibility of the key assumption of transitivity. This assumption implies that the effect modifiers' distribution is similar across treatment comparisons. Transitivity is statistically manifested through the consistency assumption which suggests that direct and indirect evidence are in agreement. Several methods have been suggested to evaluate consistency. A popular approach suggests adding inconsistency factors to the NMA model. We follow a different direction by describing each inconsistency factor with a candidate covariate whose choice relies on variable selection techniques. Our proposed method, stochastic search inconsistency factor selection (SSIFS), evaluates the consistency assumption both locally and globally, by applying the stochastic search variable selection method to determine whether the inconsistency factors should be included in the model. The posterior inclusion probability of each inconsistency factor quantifies how likely is a specific comparison to be inconsistent. We use posterior model odds or the median probability model to decide on the importance of inconsistency factors. Differences between direct and indirect evidence can be incorporated into the inconsistency detection process. A key point of our proposed approach is the construction of a reasonable "informative" prior concerning network consistency. The prior is based on the elicitation of information derived historical data from 201 published network meta-analyses. The performance of our proposed method is evaluated in two published network meta-analyses. The proposed methodology is publicly available in an R package called ssifs, published on CRAN and developed and maintained by the authors of this work.

Bayesian epidemic models for spatially aggregated count data.

Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time-varying factors. This paper addresses these issues via suitable Bayesian modelling. In doing so, we utilize a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros. The developed regression framework does incorporate serial correlation and time-varying covariates through an Ornstein-Uhlenbeck process formulation. In addition, we explore the effect of different priors, including default options and variations of mixtures of g-priors. The effect of different distance kernels for the epidemic model component is investigated. We proceed by developing branching process-based methods for testing scenarios for disease control, thus linking traditional epidemiological models with stochastic epidemic processes, useful in policy-focused decision making. The approach is illustrated with an application to a sheep pox dataset from the Evros region, Greece. Copyright © 2017 John Wiley & Sons, Ltd.

Factorial composition of the Aggression Questionnaire: a multi-sample study in Greek adults.

The primary aim of the current article was the evaluation of the factorial composition of the Aggression Questionnaire (AQ(29)) in the Greek population. The translated questionnaire was administered to the following three heterogeneous adult samples: a general population sample from Athens, a sample of young male conscripts and a sample of individuals facing problems related to substance use. Factor analysis highlighted a structure similar to the one proposed by Buss and Perry [Buss, A.F., Perry, M., 1992. The Aggression Questionnaire. Journal of Personality and Social Psychology 63, 452-459]. However, the refined 12-item version of Bryant and Smith [Bryant, F.B., Smith, B.D., 2001. Refining the architecture of aggression: a measurement model for the Buss-Perry Aggression Questionnaire. Journal of Research in Personality 35, 138-167] provided a better fit to our data. Therefore, the refined model was implemented in further analysis. Multiple group confirmatory factor analysis was applied in order to assess the variability of the 12-item AQ across gender and samples. The percentage of factor loading invariance between males and females and across the three samples defined above was high (higher than 75%). The reliability (internal consistency) of the scale was satisfactory in all cases. Content validity of the 12-item AQ was confirmed by comparison with the Symptom Check-List 90 Revised.

Mixed handedness is associated with the Disorganization dimension of schizotypy in a young male population.

Within the ASPIS (Athens Study of Psychosis Proneness and Incidence of Schizophrenia) we sought out to examine in accordance with previous reports if a deviation from dextrality is associated with an augmented endorsement of self rated schizotypal personality traits in a large population of 1129 young male army recruits. Schizotypal traits were assessed using the Schizotypal Personality Questionnaire and hand preference membership was determined by applying stringent criteria derived from the Annett Handedness Questionnaire and the Porac-Coren questionnaire of lateral preferences. By adopting three different definitions of hand preference membership, we confirmed an association between mixed handedness and increased schizotypal personality traits, and in particular with Disorganization schizotypy that encompasses aspects of self perceived difficulties in verbal communication. Non-verbal cognitive ability, as indexed by measurement of non-verbal IQ, sustained attention and working memory was not associated with hand preference. We argue that a deviation from normal cerebral lateralization, as indexed by mixed handedness, is associated with mild sub clinical language dysfunction, rather than non-verbal cognitive ability, and this might be relevant to the expression of psychosis phenotype.

Factorial composition of self-rated schizotypal traits among young males undergoing military training.

The aim of this study within the Athens Study of Psychosis Proneness and Incidence of Schizophrenia (ASPIS) was the examination of the latent structure of schizotypal dimensions among a large population of young male conscripts in the Greek Air Force during their first week of military training. Confirmatory factor analysis (CFA) was conducted on 1,355 reliable responders to the self-rated Schizotypal Personality Questionnaire (SPQ), which covers all nine aspects of DSM-III-R schizotypal personality disorder (SPD). A four-factor model (cognitive/perceptual, paranoid, negative, and disorganization schizotypal dimensions) provided a better fit to the data than did other competing models (one-, two-, three-, four, and five-factor models). This result is in agreement with recent findings supporting the notion of a multidimensional construct of the schizotypy and related schizophrenia phenotype.

Stochastic search item selection for factor analytic models.

In this paper we implement a Markov chain Monte Carlo algorithm based on the stochastic search variable selection method of George and McCulloch (1993) for identifying promising subsets of manifest variables (items) for factor analysis models. The suggested algorithm is constructed by embedding in the usual factor analysis model a normal mixture prior for the model loadings with latent indicators used to identify not only which manifest variables should be included in the model but also how each manifest variable is associated with each factor. We further extend the suggested algorithm to allow for factor selection. We also develop a detailed procedure for the specification of the prior parameters values based on the practical significance of factor loadings using ideas from the original work of George and McCulloch (1993). A straightforward Gibbs sampler is used to simulate from the joint posterior distribution of all unknown parameters and the subset of variables with the highest posterior probability is selected. The proposed method is illustrated using real and simulated data sets.