Pairwise comparisons of treatments with ordinal responses in a two-way setting

In clinical studies, pairwise comparisons are frequently performed to examine differences in efficacy between treatments. The statistical methods of pairwise comparisons are available when treatment responses are measured on an ordinal scale. The Wilcoxon–Mann–Whitney test and the latent normal model are popular examples. However, these procedures cannot be used to compare treatments in parallel groups (a two-way design) when overall type I error must be controlled. In this paper, we explore statistical approaches to the pairwise testing of treatments that satisfy the requirements of a two-way layout. The results of our simulation indicate that the latent normal approach is superior to the Wilcoxon–Mann–Whitney test. Clinical examples are used to illustrate our suggested testing methods.     

Parallel and interacting stochastic approximation annealing algorithms for global optimisation

We present the parallel and interacting stochastic approximation annealing (PISAA) algorithm, a stochastic simulation procedure for global optimisation, that extends and improves the stochastic approximation annealing (SAA) by using population Monte Carlo ideas. The efficiency of standard SAA algorithm crucially depends on its self-adjusting mechanism which presents stability issues in high dimensional or rugged optimisation problems. The proposed algorithm involves simulating a population of SAA chains that interact each other in a manner that significantly improves the stability of the self-adjusting mechanism and the search for the global optimum in the sampling space, as well as it inherits SAA desired convergence properties when a square-root cooling schedule is used. It can be implemented in parallel computing environments in order to mitigate the computational overhead. As a result, PISAA can address complex optimisation problems that it would be difficult for SAA to satisfactory address. We demonstrate the good performance of the proposed algorithm on challenging applications including Bayesian network learning and protein folding. Our numerical comparisons suggest that PISAA outperforms the simulated annealing, stochastic approximation annealing, and annealing evolutionary stochastic approximation Monte Carlo.     

Control‐based imputation for sensitivity analyses in informative censoring for recurrent event data

In clinical trials, missing data commonly arise through nonadherence to the randomized treatment or to study procedure. For trials in which recurrent event endpoints are of interests, conventional analyses using the proportional intensity model or the count model assume that the data are missing at random, which cannot be tested using the observed data alone. Thus, sensitivity analyses are recommended. We implement the control‐based multiple imputation as sensitivity analyses for the recurrent event data. We model the recurrent event using a piecewise exponential proportional intensity model with frailty and sample the parameters from the posterior distribution. We impute the number of events after dropped out and correct the variance estimation using a bootstrap procedure. We apply the method to an application of sitagliptin study.     

Racial/Ethnic Minority Vocational Research Trends: An 11‐Year Update

This study extends Flores et al.'s ( 2006 ) 36‐year analysis of racial/ethnic minority (REM) career research to identify recent trends in choice of topics and leading individual and institutional contributors. The authors identified 166 articles on REM career research published from 2005 to 2015 in the Journal of Vocational Behavior, The Career Development Quarterly, the Journal of Career Assessment, and the Journal of Career Development. Of these studies, 92% were empirical, with more than three quarters of these using quantitative rather than qualitative methodology. Most frequently, the empirical studies (n = 153) were based on samples of high school (30.7%) or undergraduate (34.6%) students. The authors discuss the implications of their findings and suggest ways to expand REM career research in the future.     

Attitudinal and behavioral differences between cigarette users who do and do not identify as “smokers”

Objective: The current research aimed to understand differences in smoking-related identity among college students and to illustrate the distinct characteristics of nonidentified smokers (NIS). Participants: Students from 2 colleges in Massachusetts (N = 538; April 2016). Methods: Respondents reported by online survey whether or not they had smoked a cigarette in the past 30 days and if they self-identified as smokers. Nonsmokers (NIS) and identified smokers (IS) were then compared on their smoking attitudes and behavior. Results: NIS made up 12.5% of the sample and 64% of all tobacco users. NIS perceived themselves as less addicted and were more confident in their ability to quit smoking. They also were less likely to smoke alone, buy their own cigarettes, or have friends who smoked. Conclusions: College health officials can best motivate NIS to quit smoking by focusing on peer norms and the potential of long-term addiction.     

Mini-max-risk and mini-mean-risk inferences for a partially piecewise regression

We consider a partially piecewise regression in which the main regression coefficients are constant in all subdomains, but the extraessential regression function is variable in different pieces and is difficult to be estimated. Under this situation, two new regression methodologies are proposed under the criteria of mini-max-risk and mini-mean-risk. The resulting models can describe the regression relations in maximum-risk and mean-risk environments, respectively. A two-stage estimation procedure, together with a composite method, is introduced. The asymptotic normality of the estimators is established, the standard convergence rate and efficiency are achieved. Some unusual features of the new estimators and predictions, and the related variable selection are discussed for a comprehensive comparison. Simulation studies and a real-financial example are given to illustrate the new methodologies.     

Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification

Here we consider a multinomial probit regression model where the number of variables substantially exceeds the sample size and only a subset of the available variables is associated with the response. Thus selecting a small number of relevant variables for classification has received a great deal of attention. Generally when the number of variables is substantial, sparsity-enforcing priors for the regression coefficients are called for on grounds of predictive generalization and computational ease. In this paper, we propose a sparse Bayesian variable selection method in multinomial probit regression model for multi-class classification. The performance of our proposed method is demonstrated with one simulated data and three well-known gene expression profiling data: breast cancer data, leukemia data, and small round blue-cell tumors. The results show that compared with other methods, our method is able to select the relevant variables and can obtain competitive classification accuracy with a small subset of relevant genes.     

Sexual dysfunction predicts depressive symptoms during the first 2 years postpartum

Background

The association between sexual function and depression has yet to be examined in a prospective cohort study with prolonged postpartum follow-up.

What Helped Nonprofits Weather the Great Recession?

This study examined the effectiveness of several financial strategies for nonprofit organizations in fighting the Great Recession. Using data from human services and community improvement organizations in the state of New Jersey, we tested hypotheses about the relationships between three measures of financial sustainability and various fundraising efforts and financial indicators. We found that (1) except for strong external funding relationships, funding efforts were generally not effective in enhancing financial sustainability during the crisis; (2) higher operating margin and equity ratio improved an organization's ability to generate revenue and maintain expense levels, but higher debt ratio and administrative cost ratio were generally harmful; and (3) revenue diversification might aggravate fiscal stress and cause more expense cuts in a severe crisis. These findings improve our understanding of the mechanics of nonprofit management during times of harsh fiscal conditions and suggest useful ways for nonprofit organizations to navigate future financial crises.