A procedure for the change point problem in parametric models based on phi-divergence test-statistics

This paper studies the change point problem for a general parametric, univariate or multivariate family of distributions. An information theoretic procedure is developed which is based on general divergence measures for testing the hypothesis of the existence of a change. For comparing the accuracy of the new test-statistic a simulation study is performed for the special case of a univariate discrete model. Finally, the procedure proposed in this paper is illustrated through a classical change-point example

Prenatal cocaine exposure and its impact on cognitive functions of offspring: A pathophysiological insight

It is estimated that approximately 0.5%-3% of fetuses are prenatally exposed to cocaine (COC). The neurodevelopmental implications of this exposure are numerous and include motor skill impairments, alterations of social function, predisposition to anxiety, and memory function and attention deficits; these implications are commonly observed in experimental studies and ultimately affect both learning and IQ. According to previous studies, the clinical manifestations of prenatal COC exposure seem to persist at least until adolescence. The pathophysiological cellular processes that underlie these impairments include dysfunctional myelination, disrupted dendritic architecture, and synaptic alterations. On a molecular level, various neurotransmitters such as serotonin, dopamine, catecholamines, and γ-Aminobutyric acid seem to participate in this process. Finally, prenatal COC abuse has been also associated with functional changes in the hormones of the hypothalamic-pituitary-Adrenal axis that mediate neuroendocrine responses. The purpose of this review is to summarize the neurodevelopmental consequences of prenatal COC abuse, to describe the pathophysiological pathways that underlie these consequences, and to provide implications for future research in the field. © 2016 by De Gruyter

Change-point detection in multinomial data using phi-divergence test statistics

We propose two families of maximally selected phi-divergence tests to detect a change in the probability vectors of a sequence of multinomial random variables with possibly different sizes. In addition, the proposed statistics can be used to estimate the location of the change-point. We derive the limit distributions of the proposed statistics under the no change null hypothesis. One of the families has an extreme value limit. The limit of the other family is the maximum of the norm of a multivariate Brownian bridge. We check the accuracy of these limit distributions in case of finite sample sizes. A Monte Carlo analysis shows the possibility of improving the behavior of the test statistics based on the likelihood ratio and chi-square tests introduced in Horvath and Serbinowska [7]. The classical Lindisfarne Scribes problem is used to demonstrate the applicability of the proposed statistics to real life data sets

Effectiveness and safety of intracranial events associated with the use of direct oral anticoagulants for atrial fibrillation: A systematic review and meta-analysis of 92 studies

Aims: Observational studies have investigated the effectiveness and safety of direct oral anticoagulants (DOACs) and vitamin K antagonists (VKAs) used in nonvalvular atrial fibrillation. We performed a systematic review and meta-analysis assessing the risk of ischaemic stroke, thromboembolism (TE) and intracranial haemorrhage (ICH) associated with the use of DOACs and VKAs. Methods: Medline and Embase were systematically searched until April 2021. Observational studies were gathered and hazard ratios (HRs) with 95% confidence intervals (CI) were extracted. Subgroup analyses based on DOAC doses, history of chronic kidney disease, stroke, exposure to VKA, age and sex were performed. A random-effects model was used. Results: We included 92 studies and performed 107 comparisons. Apixaban was associated with lower risk of stroke (HR: 0.82, 95% CI: 0.68–0.99) compared to dabigatran. Rivaroxaban was associated with lower risk of stroke (HR: 0.90, 95% CI: 0.83–0.98) compared to VKA. Dabigatran (HR: 0.85, 95% CI: 0.80–0.91), rivaroxaban (HR: 0.83, 95% CI: 0.77–0.89) and apixaban (HR: 0.75, 95% CI: 0.65–0.86) were associated with lower risk for TE/stroke compared to VKA. Apixaban (HR: 1.32, 95% CI: 1.03–1.68) and rivaroxaban (HR: 1.58, 95% CI: 1.31–1.89) were associated with higher risk of ICH compared to dabigatran. Dabigatran (HR: 0.48, 95% CI: 0.44–0.52), apixaban (HR: 0.60, 95% CI: 0.49–0.73) and rivaroxaban (HR: 0.73, 95% CI: 0.65–0.81) were associated with lower risk of ICH compared to VKA. Conclusion: Our study demonstrated significant differences in the risk of ischaemic stroke, TE/stroke and ICH associated with individual DOACs compared to both other DOACs and VKA. © 2022 British Pharmacological Society

Multivariate linear regression with non-normal errors: a solution based on mixture models

In some situations, the distribution of the error terms of a multivariate linear regression model may depart from normality. This problem has been addressed, for example, by specifying a different parametric distribution family for the error terms, such as multivariate skewed and/or heavy-tailed distributions. A new solution is proposed, which is obtained by modelling the error term distribution through a finite mixture of multi-dimensional Gaussian components. The multivariate linear regression model is studied under this assumption. Identifiability conditions are proved and maximum likelihood estimation of the model parameters is performed using the EM algorithm. The number of mixture components is chosen through model selection criteria; when this number is equal to one, the proposal results in the classical approach. The performances of the proposed approach are evaluated through Monte Carlo experiments and compared to the ones of other approaches. In conclusion, the results obtained from the analysis of a real dataset are presented