Comparative efficacy of imagery rehearsal therapy and prazosin in the treatment of trauma-related nightmares in adults: A meta-analysis of randomized controlled trials

Pharmacological treatment with prazosin and psychological treatment with imagery rehearsal therapy (IRT) are the two main treatments of posttraumatic nightmares. The American Academy of Sleep Medicine task force recently listed IRT as the recommended treatment for trauma-related nightmares and changed the recommendation of prazosin to ‘may be used’. This new recommendation was based on a single prazosin trial and not on a meta-analytic review of all available trials. The current meta-analysis aims to fill this gap in the literature. Eight studies on IRT and seven studies on prazosin (N = 1.078) were analyzed based on the random effects model. Relative to control groups, prazosin had a moderate to large effect on nightmare frequency (g = 0.61), posttraumatic stress symptoms (g = 0.81), and sleep quality (g = 0.85). IRT showed small to moderate effects on nightmare frequency (g = 0.51), posttraumatic symptoms (g = 0.31), and sleep quality (g = 0.51). No significant differences in effect were observed between prazosin and IRT on any of these outcomes (all p's > 0.10). It is concluded that downgrading the recommendation of prazosin may be a premature decision and that the aggregated results in this meta-analysis clearly show efficacy of both treatments

Suppressing microdata to prevent classification based inference

The revolution of the Internet together with the progression in computer technology makes it easy for institutions to collect an unprecedented amount of personal data. This pervasive data collection rally coupled with the increasing necessity of dissemination and sharing of non-aggregated data, i.e., microdata, raised a lot of concerns about privacy. One method to ensure privacy is to selectively hide the confidential, i.e. sensitive, information before disclosure. However, with data mining techniques, it is now possible for an adversary to predict the hidden confidential information from the disclosed data sets. In this paper, we concentrate on one such data mining technique called classification. We extend our previous work on microdata suppression to prevent both probabilistic and decision tree classification based inference. We also provide experimental results showing the effectiveness of not only the proposed methods but also the hybrid methods, i.e., methods suppressing microdata against both classification models, on real-life data sets

Network Models to Organize a Dispersed Literature: The Case of Misunderstanding Analysis of Covariance

We outline a network method to synthesize a literature overview from search results obtained by multiple team members. Several network statistics are used to create a single representativeness ranking. We illustrate the method with the dispersed literature on a common misinterpretation of analysis of covariance (ANCOVA). The network method yields a top ten list of the most relevant articles that students and researchers can take as a point of departure for a more detailed study on this topic. The proposed methodology is implemented in Shiny, an open-source R package