Anxiety and depression among college students with attention-deficit/hyperactivity disorder (ADHD): Cross-informant,sex, and subtype differences

Objective: This study examined symptoms of anxiety and depression among college students with attention-deficit/hyperactivity disorder (ADHD). Participants: Data were collected between March 2011 and March 2016 from 150 college students with ADHD and 150 college students without ADHD. Method: Participants with ADHD were compared to a sex- and ethnicity-matched control group. For the ADHD group, parent and self-report of anxiety and depression were also compared. Results: College students with ADHD self-reported significantly higher anxiety and depressive symptoms than did students without ADHD. Scores on parent-report measures of anxiety and depressive symptoms were significantly higher than scores on self-report measures. Significant sex differences were found for participants with ADHD, with females showing higher depressive and anxiety symptoms than males. Parent-reported anxiety symptoms were higher for those with inattentive type ADHD compared to combined type ADHD. Conclusion: The current study highlights the importance of multi-informant assessment in ADHD evaluations for college-aged adults.     

Deception in Family Therapy: Recognition,Implications, and Intervention

Deception, in its many forms, is present in families and relationships across the globe. The impact of the presence and discovery of family secrets or deceptive acts on the individual, the family unit, and the therapeutic process, particularly in the field of marriage and family therapy, has not been thoroughly studied or discussed. Further, deceptive acts during the therapeutic process may have implications for effective treatment planning. This article defines the various forms of deception and its systemic effects on relationships and on the therapeutic process. Possible interventions to address deception in a family therapeutic setting are proposed.     

Participatory Spaces of Mental Health Service User Organizations in the Post-deinstitutional Era: Mapping Roles and Challenges

By applying the concept of participatory spaces, this article maps and analyzes current research on mental health service user organizations (MHSUOs). We have analyzed research literature from 2006 to 2016 to examine how the role of and challenges facing MHSUOs are formulated in the post-deinstitutional era. The current situation is marked by MHSUOs parallel presence in invited, claimed and popular spaces for participation. The post-deinstitutional era is characterized by a shift in focus from gaining access to such participatory spaces, to critically examining the political opportunities available in these. We further argue that the dominance of psychiatry-specific spaces could prevent MHSUOs from fully exploring their potential for participation in broader social issues.     

Covariance-regularized regression and classification for high dimensional problems

Summary.  We propose covariance-regularized regression, a family of methods for prediction in high dimensional settings that uses a shrunken estimate of the inverse covariance matrix of the features to achieve superior prediction. An estimate of the inverse covariance matrix is obtained by maximizing the log-likelihood of the data, under a multivariate normal model, subject to a penalty; it is then used to estimate coefficients for the regression of the response onto the features. We show that ridge regression, the lasso and the elastic net are special cases of covariance-regularized regression, and we demonstrate that certain previously unexplored forms of covariance-regularized regression can outperform existing methods in a range of situations. The covariance-regularized regression framework is extended to generalized linear models and linear discriminant analysis, and is used to analyse gene expression data sets with multiple class and survival outcomes.     

SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology.     

VARIATIONAL BAYESIAN ANALYSIS FOR HIDDEN MARKOV MODELS

The variational approach to Bayesian inference enables simultaneous estimation of model parameters and model complexity. An interesting feature of this approach is that it also leads to an automatic choice of model complexity. Empirical results from the analysis of hidden Markov models with Gaussian observation densities illustrate this. If the variational algorithm is initialized with a large number of hidden states, redundant states are eliminated as the method converges to a solution, thereby leading to a selection of the number of hidden states. In addition, through the use of a variational approximation, the deviance information criterion for Bayesian model selection can be extended to the hidden Markov model framework. Calculation of the deviance information criterion provides a further tool for model selection, which can be used in conjunction with the variational approach.     

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model. The authors would like to thank the editor and referees for their helpful comments. This work was supported by CNPq, Brazil.     

Parametric non-mixture cure models for schedule finding of therapeutic agents

We propose a phase I clinical trial design that seeks to determine the cumulative safety of a series of administrations of a fixed dose of an investigational agent. In contrast with traditional phase I trials that are designed solely to find the maximum tolerated dose of the agent, our design instead identifies a maximum tolerated schedule that includes a maximum tolerated dose as well as a vector of recommended administration times. Our model is based on a non-mixture cure model that constrains the probability of dose limiting toxicity for all patients to increase monotonically with both dose and the number of administrations received. We assume a specific parametric hazard function for each administration and compute the total hazard of dose limiting toxicity for a schedule as a sum of individual administration hazards. Throughout a variety of settings motivated by an actual study in allogeneic bone marrow transplant recipients, we demonstrate that our approach has excellent operating characteristics and performs as well as the only other currently published design for schedule finding studies. We also present arguments for the preference of our non-mixture cure model over the existing model.     

On estimating the distribution of data envelopment analysis efficiency scores: an application to nursing homes’ care planning process

Data envelopment analysis (DEA) is a deterministic econometric model for calculating efficiency by using data from an observed set of decision-making units (DMUs). We propose a method for calculating the distribution of efficiency scores. Our framework relies on estimating data from an unobserved set of DMUs. The model provides posterior predictive data for the unobserved DMUs to augment the frontier in the DEA that provides a posterior predictive distribution for the efficiency scores. We explore the method on a multiple-input and multiple-output DEA model. The data for the example are from a comprehensive examination of how nursing homes complete a standardized mandatory assessment of residents.