Initially labeled as internet addiction in the mid-1990s (e.g., Griffiths, 1996 ; Young, 1996 ), researchers have since focused on how specific online activities result in negative consequences for those who overuse and have problems with online applications such as online gambling and online sex ( Griffiths, 2000 ; Potenza, 2017 ). More recently, this has been applied to online problematic video game play, often used synonymously with terms such as online video game addiction, online gaming addiction, and Internet gaming disorder (IGD). With the publication of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association [APA], 2013 ), IGD was identified by the APA as warranting further study. The current proposed diagnostic criterion in the DSM-5 requires the presence of five of nine symptoms over a 12-month period. These include: (a) preoccupation or obsession with Internet games, (b) withdrawal symptoms when not playing Internet games, (c) an increasing need over time to spend more and more time playing video games, (d) failed attempts to stop or curb Internet gaming, (e) loss of interest in other activities such as hobbies, (f) continued overuse of Internet games even with knowledge of the impact of overuse on their life, (g) lying about extent of Internet game usage, (h) uses Internet games to relieve anxiety or guilt, and (i) has lost or put at risk an opportunity or relationship because of Internet games ( American Psychiatric Association [APA], 2013 ). However, it is unclear if the disorder represents addiction to the internet or if IGD evaluates specific behaviors occurring within the context of the video gaming ( Starcevic and Billieux, 2017 ; Young and Brand, 2017 ).
Females in empirically based peer-reviewed studies of internet gaming disorder are underrepresented, despite evidence that there are only minor gender disparities present in online gaming. Moreover, few studies have specifically evaluated adult gender effects, within a formal diagnosis of internet gaming disorder, and behavioral motivation, as defined by the reinforcing behavioral function. A mediation analysis evaluated the relationship between gender, behavioral motivation, and the diagnostic features in online gaming among adults to understand the impact of motivation on videogame playing. The present study interviewed 304 adults (aged over 18 years) in which 178 identified as female. Participants completed the Video Game Functional Assessment-Revised (VGFA-R) and the 20-item Internet Gaming Disorder Test (IGDT-20) via an online survey. Results showed that number of hours played per week, and sub-factors of the VGFA-R differed between gender, indicating that the function and the maintaining of videogame play is essential in evaluating videogame addiction. These findings support and extend the literature's limited findings concerning gender and online gaming.
A reciprocal LASSO (rLASSO) regularization employs a decreasing penalty function as opposed to conventional penalization approaches that use increasing penalties on the coefficients, leading to stronger parsimony and superior model selection relative to traditional shrinkage methods. Here we consider a fully Bayesian formulation of the rLASSO problem, which is based on the observation that the rLASSO estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters are assigned independent inverse Laplace priors. Bayesian inference from this posterior is possible using an expanded hierarchy motivated by a scale mixture of double Pareto or truncated normal distributions. On simulated and real datasets, we show that the Bayesian formulation outperforms its classical cousin in estimation, prediction, and variable selection across a wide range of scenarios while offering the advantage of posterior inference. Finally, we discuss other variants of this new approach and provide a unified framework for variable selection using flexible reciprocal penalties. All methods described in this article are publicly available as an R package at: https://github.com/himelmallick/BayesRecipe.
Summary With the availability of limited resources, innovation for improved statistical method for the design and analysis of randomized controlled trials (RCTs) is of paramount importance for newer and better treatment discovery for any therapeutic area. Although clinical efficacy is almost always the primary evaluating criteria to measure any beneficial effect of a treatment, there are several important other factors (e.g., side effects, cost burden, less debilitating, less intensive, etc.), which can permit some less efficacious treatment options favorable to a subgroup of patients. This leads to non-inferiority (NI) testing. The objective of NI trial is to show that an experimental treatment is not worse than an active reference treatment by more than a pre-specified margin. Traditional NI trials do not include a placebo arm for ethical reason; however, this necessitates stringent and often unverifiable assumptions. On the other hand, three-arm NI trials consisting of placebo, reference, and experimental treatment, can simultaneously test the superiority of the reference over placebo and NI of experimental treatment over the reference. In this article, we proposed both novel Frequentist and Bayesian procedures for testing NI in the three-arm trial with Poisson distributed count outcome. RCTs with count data as the primary outcome are quite common in various disease areas such as lesion count in cancer trials, relapses in multiple sclerosis, dermatology, neurology, cardiovascular research, adverse event count, etc. We first propose an improved Frequentist approach, which is then followed by it’s Bayesian version. Bayesian methods have natural advantage in any active-control trials, including NI trial when substantial historical information is available for placebo and established reference treatment. In addition, we discuss sample size calculation and draw an interesting connection between the two paradigms.
With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to the more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by borrowing information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner
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