Semiparametric Efficient Estimation in the Generalized Odds-Rate Class of Regression Models for Right-Censored Time-to-Event Data

The generalized odds-rate class of regression models for time to event data is indexed by a non-negative constant and assumes thatg(S(t|Z)) = (t) + Zwhere g(s) = log(^-1(s-) for > 0, g₀(s) = log(- log s), S(t|Z) is the survival function of the time to event for an individual with qx1 covariate vector Z, is a qx1 vector of unknown regression parameters, and (t) is some arbitrary increasing function of t. When =0, this model is equivalent to the proportional hazards model and when =1, this model reduces to the proportional odds model. In the presence of right censoring, we construct estimators for and exp((t)) and show that they are consistent and asymptotically normal. In addition, we show that the estimator for is semiparametric efficient in the sense that it attains the semiparametric variance bound.     

Staffing a Multiskilled Workforce with Varying Levels of Productivity: An Analysis of Cross-training Policies*

Service operations that utilize cross-trained employees face complex workforce staffing decisions that have important implications for both cost and productivity. These decisions are further complicated when cross-trained employees have different productivity levels in multiple work activity categories. A method for policy analysis in such environments can be beneficial in determining low-cost staffing plans with appropriate cross-training configurations. In this paper, we present an integer linear programming model for evaluating cross-training configurations at the policy level. The objective of the model is to minimize workforce staffing costs subject to the satisfaction of minimum labor requirements across a planning horizon of a single work shift. The model was used to evaluate eight cross-training structures (consisting of 36 unique cross-training configurations) across 512 labor requirement patterns. These structures, as well as the labor requirement patterns, were established based on data collected from maintenance operations at a large paper mill in the United States. The results indicate that asymmetric cross-training structures that permit chaining of employee skill classes across work activity categories are particularly useful.     

Statistical considerations for design and analysis of stability,comparability and formulation tests

This article considers designed experiments for stability, comparability, and formulation testing that are analyzed with regression models in which the degradation rate is a fixed effect. In this setting, we investigate how the number of lots, the number of time points and their locations affect the precision of the entities of interest, leverages of the time points, detection of non-linearity and interim analyses. This investigation shows that modifying time point locations suggested by ICH for stability studies can significantly improve these objectives. In addition, we show that estimates of precision can be biased when a regression model that assumes independent measurements is used in the presence of within-assay session correlation. This bias can lead to longer shelf life estimates in stability studies and loss of power in comparability studies. Mixed-effect models that take into account within-assay session correlation are shown to reduce this bias. The findings in this article are obtained from well known statistical theory but provide valuable practical advice to scientists and statisticians designing and interpreting these types of experiments.     

Bayesian semiparametric modeling and inference with mixtures of?symmetric distributions

We propose a semiparametric modeling approach for mixtures of symmetric distributions. The mixture model is built from a common symmetric density with different components arising through different location parameters. This structure ensures identifiability for mixture components, which is a key feature of the model as it allows applications to settings where primary interest is inference for the subpopulations comprising the mixture. We focus on the two-component mixture setting and develop a Bayesian model using parametric priors for the location parameters and for the mixture proportion, and a nonparametric prior probability model, based on Dirichlet process mixtures, for the random symmetric density. We present an approach to inference using Markov chain Monte Carlo posterior simulation. The performance of the model is studied with a simulation experiment and through analysis of a rainfall precipitation data set as well as with data on eruptions of the Old Faithful geyser.     

Free energy methods for Bayesian inference: efficient exploration of univariate Gaussian mixture posteriors

Because of their multimodality, mixture posterior distributions are difficult to sample with standard Markov chain Monte Carlo (MCMC) methods. We propose a strategy to enhance the sampling of MCMC in this context, using a biasing procedure which originates from computational Statistical Physics. The principle is first to choose a “reaction coordinate”, that is, a “direction” in which the target distribution is multimodal. In a second step, the marginal log-density of the reaction coordinate with respect to the posterior distribution is estimated; minus this quantity is called “free energy” in the computational Statistical Physics literature. To this end, we use adaptive biasing Markov chain algorithms which adapt their targeted invariant distribution on the fly, in order to overcome sampling barriers along the chosen reaction coordinate. Finally, we perform an importance sampling step in order to remove the bias and recover the true posterior. The efficiency factor of the importance sampling step can easily be estimated a priori once the bias is known, and appears to be rather large for the test cases we considered.     

Identifying Male College Students' Perceived Health Needs,Barriers to Seeking Help,and Recommendations to Help Men Adopt Healthier Lifestyles

Abstract

Seven focus groups at a university campus were formed to identify college men's health concerns, barriers to seeking help, and recommendations to help college men adopt healthier lifestyles. Content analysis was used to identify and organize primary patterns in the focus-group data. Results of the study revealed that the college men were aware that they had important health needs but took little action to address them. The participants identified both physical and emotional health concerns. Alcohol and substance abuse were rated as the most important issues for men. The greatest barrier to seeking services was the men's socialization to be independent and conceal vulnerability. The most frequently mentioned suggestions for helping men adopt healthier lifestyles were offering health classes, providing health information call-in service, and developing a men's center. Implications of the results are discussed.     

Liberal Education and Social Welfare

The author presents the vulnerability, risk, and resilience model as a mechanism for improved content integration in the Human Behavior and the Social Environment sequence in social work education. The model demonstrates the interaction of community and social context variables with individual characteristics in the development of social work problems. The five steps in the model are (1) define the social work problem of concern, (2) identify risk and protective factors in the social context, (3) identify intrinsic vulnerability and invulnerability characteristics, (4) intersect these two, and (5) explore intervention: and prevention options. In addition, she provides a rationale and background for the model, describes the model itself, and develops an example from social work practice (adolescent substance abuse) for social work education.     

North Americans' Attitudes Toward Illegal Drugs

Public opinion polls conducted from 1969 to 2003 found a variety of opinions on the use, possession, and legalization of marijuana. Public opinion about legalizing marijuana use is increasing, but it is also increasing toward harsher penalties for those who possess a small amount of marijuana. Public opinion regarding the legalization of illegal drugs appears to be influenced by the times. The majority of Americans increasingly believe that the country has made some progress in dealing with the problems of illegal drugs, but Americans are also reporting more difficulty within their families related to marijuana. This article examines trends based in public opinion polls on the use of marijuana in the United States and Canada and discusses the social and political history of marijuana and the biophysical effects, medical uses, definitions, and previous research related to marijuana.