Prazosin showed medium-to-large and statistically significant effects on PTSD symptoms in general and sleep disturbances in particular. While promising, results should be interpreted with caution given the limited total number of participants and the limitations induced by the majority of participants being male and noncivilian.

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Radiomics is an emerging field of medical image analysis research where quantitative measurements are obtained from radiological images that can be utilized to predict patient outcomes and inform treatment decisions. Cancer patients routinely undergo radiological evaluations when images of various modalities including computed tomography, positron emission tomography, and magnetic resonance images are collected for diagnosis and for evaluation of disease progression. Tumor characteristics, often referred to as measures of tumor heterogeneity, can be computed using these clinical images and used as predictors of disease progression and patient survival. Several approaches for quantifying tumor heterogeneity have been proposed, including intensity histogram‐based measures, shape and volume‐based features, and texture analysis. Taking into account the topology of the tumors we propose a statistical framework for estimating tumor heterogeneity using clustering based on Markov random field theory. We model the voxel intensities using a Gaussian mixture model using a Gibbs prior to incorporate voxel neighborhood information. We propose a novel approach to choosing the number of mixture components. Subsequently, we show that the proposed procedure outperforms the existing approaches when predicting lung cancer survival.

Established as a rigorous pedagogical device at Harvard University, the case method has grown into an indispensable mode of instruction at many business schools. Its effectiveness has been praised for increasing student participation during in-class discussions, providing hands-on engagement in real-world business problems, and increasing long-term retention rates. This article illustrates how novel case studies that mimic real-life statistical consulting engagements can be incorporated in the curriculum of an undergraduate, introductory time series course. The assessment of learning objectives as well as pedagogical implications when teaching using statistical consulting case studies is elucidated. The article also lays out guidelines for adopting statistical consulting case studies should the readers choose to incorporate the case method into the curricula of courses that they teach. A sample case study which the author has successfully used in his classroom instruction is provided in this article.

Data used for monitoring and control of industrial processes are often best modeled as a time series. An important issue is to determine whether such time series are stationary. In this article we discuss the variogram-a graphical tool for assessing stationarity. We build on previous work and provide further details and more general results including analytical structures of variogram for various non-stationary processes, and illustrate with a number of examples of variograms using standard data sets from the literature and simulated data sets.