Recipes for Attention: Policy Reforms,Crises, Organizational Characteristics,and the Newspaper Coverage of the LGBT Movement, 1969–2009

Why do some organizations in a movement seeking social change gain extensive national newspaper coverage? To address the question, we innovate in theoretical and empirical ways. First, we elaborate a theoretical argument that builds from the political mediation theory of movement consequences and incorporates the social organization of newspaper practices. This media and political mediation model integrates political and media contexts and organizations' characteristics and actions. With this model, we hypothesize two main routes to coverage: one that includes changes in public policy and involves policy‐engaged, well‐resourced, and inclusive organizations and a second that combines social crises and protest organizations. Second, we appraise these arguments with the first analysis of the national coverage of all organizations in a social movement over its career: 84 lesbian, gay, bisexual, and transgender rights and AIDS‐related organizations in the New York Times, Los Angeles Times, and Wall Street Journal from 1969 to 2010. These analyses go beyond previous research that provides either snapshots of many organizations at one point in time or overtime analyses of aggregated groups of organizations or individual organizations. The results of both historical and fuzzy set qualitative comparative analyses support our media and political mediation model.     

The Rheumatoid Arthritis Drug Development Model: a case study in Bayesian clinical trial simulation

The development of a new drug is a major undertaking and it is important to consider carefully the key decisions in the development process. Decisions are made in the presence of uncertainty and outcomes such as the probability of successful drug registration depend on the clinical development programmme. The Rheumatoid Arthritis Drug Development Model was developed to support key decisions for drugs in development for the treatment of rheumatoid arthritis. It is configured to simulate Phase 2b and 3 trials based on the efficacy of new drugs at the end of Phase 2a, evidence about the efficacy of existing treatments, and expert opinion regarding key safety criteria. The model evaluates the performance of different development programmes with respect to the duration of disease of the target population, Phase 2b and 3 sample sizes, the dose(s) of the experimental treatment, the choice of comparator, the duration of the Phase 2b clinical trial, the primary efficacy outcome and decision criteria for successfully passing Phases 2b and 3. It uses Bayesian clinical trial simulation to calculate the probability of successful drug registration based on the uncertainty about parameters of interest, thereby providing a more realistic assessment of the likely outcomes of individual trials and sequences of trials for the purpose of decision making. In this case study, the results show that, depending on the trial design, the new treatment has assurances of successful drug registration in the range 0.044–0.142 for an ACR20 outcome and 0.057–0.213 for an ACR50 outcome. Copyright © 2009 John Wiley & Sons, Ltd.     

Natural Increase: A New Source of Population Growth in Emerging Hispanic Destinations in the United States

Updated US Census Bureau estimates and race/ethnic‐specific birth and death data for the post‐2000 period are used to highlight the increasing role of natural increase as an engine of population growth in emerging Hispanic destinations. Newly emerging Hispanic growth areas are distinguished from established and high‐growth areas from the 1990s. The findings document that recent Hispanic population gains have been generated increasingly by natural increase—the excess of Hispanic births over deaths. Hispanics accounted for 46 percent of the population gain and 53 percent of the natural increase in nonmetro America in 2000–2005. Yet, Hispanics represented only 5.4 percent of the nonmetro population in 2000. In metro areas, they accounted for 50 percent of the population gain and 47 percent of the natural increase, although they comprised only 14 percent of the metro population. Current trends suggest that the ascendancy of the US Hispanic population is likely to continue unabated, whether restrictive immigration legislation is enacted or not. The growth of the Hispanic population, caused increasingly by natural increase, has taken on a demographic momentum of its own.     

The impact of specific occupation on mortality in the U.S. National Longitudinal Mortality Study

We compare mortality differences for specific and general categories of occupations using a national cohort of approximately 380,000 persons aged 25-64 from the U.S. National Longitudinal Mortality Study. Based on comparisons of relative risk obtained from Cox proportional-hazards model analyses, higher risk is observed in moving across the occupational spectrum from the technical, highly skilled occupations to less-skilled and generally more labor-intensive occupations. Mortality differences obtained for social status groups of specific occupations are almost completely accounted for by adjustments for income and education. Important differences are shown to exist for selected specific occupations beyond those accounted for by social status, income, and education. High-risk specific occupations include taxi drivers, cooks, longshoremen, and transportation operatives. Low-risk specific occupations include lawyers, natural scientists, teachers, farmers, and a variety of engineers.     

A composite quantile function estimator with applications in bootstrapping

In this note we define a composite quantile function estimator in order to improve the accuracy of the classical bootstrap procedure in small sample setting. The composite quantile function estimator employs a parametric model for modelling the tails of the distribution and uses the simple linear interpolation quantile function estimator to estimate quantiles lying between 1/(n+1) and n/(n+1). The method is easily programmed using standard software packages and has general applicability. It is shown that the composite quantile function estimator improves the bootstrap percentile interval coverage for a variety of statistics and is robust to misspecification of the parametric component. Moreover, it is also shown that the composite quantile function based approach surprisingly outperforms the parametric bootstrap for a variety of small sample situations.     

Stabilizing bootstrap‐t confidence intervals for small samples

A major use of the bootstrap methodology is in the construction of nonparametric confidence intervals. Although no consensus has yet been reached on the best way to proceed, theoretical and empirical evidence indicate that bootstra.‐t intervals provide a reasonable solution to this problem. However, when applied to small data sets, these intervals can be unusually wide and unstable. The author presents techniques for stabilizing bootstra.‐t intervals for small samples. His methods are motivated theoretically and investigated though simulations.