Organizational Compatibility and Workplace Drug Testing: Modeling the Adoption of Innovative Social Control Practices

Few researchers have examined organizational variation in the adoption of workplace drug testing, but innovation theory suggests that adoption is more likely when it is compatible with an organization's values, previously introduced ideas, and needs. Using data from the 1997 National Employee Survey, this research models the effects of organizational compatibility, industry, and establishment size on the adoption of drug testing. The data reveal that compatibility, as measured by an organization's rules orientation, presence of an employee assistance program, and mechanization, is associated with the adoption of drug testing. As predicted, the adoption of drug testing varies across industries and by establishment size.     

Health care issues among lesbian, gay, bisexual, transgender and intersex (LGBTI) populations in the United States: Introduction

This supplemental issue of the Journal of Homosexuality presents research that explores a variety of health care issues encountered by lesbian, gay, bisexual, transgender and intersex (LGBTI) population groups in the United States over the 10-year period from 1993 to 2002. Topics include access to health care, utilization of care, training of medical and mental health providers, and the appropriate preparation of clinical offices and waiting areas. Authors used a variety of community-based public health research methods, including participant and provider surveys and retrospective chart reviews of patients, to develop this body of research, providing a recent-historical perspective on the complex health care and health-related needs of sexual and gender minorities. Particularly for transgender and intersex populations, the state of research describing their health care needs is in its infancy, and much remains to be done to design effective medical and mental health programs and interventions.     

The potential impact of discrimination fears of older gays, lesbians, bisexuals and transgender individuals living in small- to moderate-sized cities on long-term health care

As an exploration of the potential impact of fears of discrimination against GLBTs in long-term health care settings, this study compared perceptions of GLBT persons and heterosexuals. A total of 132 GLBT persons and 187 heterosexuals living in Eastern Washington completed a survey that contained demographic questions and perceptions of discrimination in long-term care settings. Most respondents suspected that staff and residents of care facilities discriminate against GLBTs. GLBT respondents who believed that residents of care facilities are victims of discrimination were more likely to believe that they would have to hide their sexual orientation if admitted to a care facility. GLBT respondents were more likely than heterosexual respondents to believe that GLBTs do not have equal access to health care and social services, that GLBTs residents of care facilities are victims of discrimination, that GLBT sensitivity training programs would benefit staff and residents of care facilities, and that GLBT retirement facilities would be a positive development for older GLBTs. This study is offered as a preliminary investigation of concerns about GLBT discrimination in health care settings, how concerns are expressed, and the implications of those concerns for health care needs.     

A tutorial on rank-based coefficient estimation for censored data in small- and large-scale problems

The analysis of survival endpoints subject to right-censoring is an important research area in statistics, particularly among econometricians and biostatisticians. The two most popular semiparametric models are the proportional hazards model and the accelerated failure time (AFT) model. Rank-based estimation in the AFT model is computationally challenging due to optimization of a non-smooth loss function. Previous work has shown that rank-based estimators may be written as solutions to linear programming (LP) problems. However, the size of the LP problem is O(n ²+p) subject to n ² linear constraints, where n denotes sample size and p denotes the dimension of parameters. As n and/or p increases, the feasibility of such solution in practice becomes questionable. Among data mining and statistical learning enthusiasts, there is interest in extending ordinary regression coefficient estimators for low-dimensions into high-dimensional data mining tools through regularization. Applying this recipe to rank-based coefficient estimators leads to formidable optimization problems which may be avoided through smooth approximations to non-smooth functions. We review smooth approximations and quasi-Newton methods for rank-based estimation in AFT models. The computational cost of our method is substantially smaller than the corresponding LP problem and can be applied to small- or large-scale problems similarly. The algorithm described here allows one to couple rank-based estimation for censored data with virtually any regularization and is exemplified through four case studies.     

Bayesian Binomial Regression: Predicting Survival at a Trauma Center

Standard methods for analyzing binomial regression data rely on asymptotic inferences. Bayesian methods can be performed using simple computations, and they apply for any sample size. We provide a relatively complete discussion of Bayesian inferences for binomial regression with emphasis on inferences for the probability of “success.” Furthermore, we illustrate diagnostic tools, perform model selection among nonnested models, and examine the sensitivity of the Bayesian methods.     

Automatic locally adaptive smoothing for tree-based set estimation

Tree-based methods similar to CART have recently been utilized for problems in which the main goal is to estimate some set of interest. It is often the case that the boundary of the true set is smooth in some sense, however tree-based estimates will not be smooth, as they will be a union of ‘boxes’. We propose a general methodology for smoothing such sets that allows for varying levels of smoothness on the boundary automatically. The method is similar to the idea underlying support vector machines, which is applying a computationally simple technique to data after a non-linear mapping to produce smooth estimates in the original space. In particular, we consider the problem of level-set estimation for regression functions and the dyadic tree-based method of Willett and Nowak [Minimax optimal level-set estimation, IEEE Trans. Image Process. 16 (2007), pp. 2965–2979].     

Book Reviews

This article examines some difficulties associated with the Haynes–Stone model and attempts to clarify how the model can be justified as representing the hypothesis that “quantity is demand determined and price is supply determined.” It also argues that the applications of such a model as was done by Haynes and Stone might not have resolved the controversies related to the Phillips curve and the supply function of exports (or imports).