Social Policy and Disability: Some Theoretical Issues

This paper suggests that current ways of thinking about disability are inadequate, both theoretically and as a basis for social policy. The main reason for this is that most writers have remained locked within a 'personal tragedy theory of disability' and have failed to examine the concept of disability critically. Even those writers who have attempted to take disability seriously have failed to move beyond a traditional framework and have subsequently produced sterile accounts. The final section of the paper begins to suggest what a social theory of disability might look like.     

Missing data: Discussion points from the PSI missing data expert group

The Points to Consider Document on Missing Data was adopted by the Committee of Health and Medicinal Products (CHMP) in December 2001. In September 2007 the CHMP issued a recommendation to review the document, with particular emphasis on summarizing and critically appraising the pattern of drop‐outs, explaining the role and limitations of the ‘last observation carried forward’ method and describing the CHMP's cautionary stance on the use of mixed models. In preparation for the release of the updated guidance document, statisticians in the Pharmaceutical Industry held a one‐day expert group meeting in September 2008. Topics that were debated included minimizing the extent of missing data and understanding the missing data mechanism, defining the principles for handling missing data and understanding the assumptions underlying different analysis methods. A clear message from the meeting was that at present, biostatisticians tend only to react to missing data. Limited pro‐active planning is undertaken when designing clinical trials. Missing data mechanisms for a trial need to be considered during the planning phase and the impact on the objectives assessed. Another area for improvement is in the understanding of the pattern of missing data observed during a trial and thus the missing data mechanism via the plotting of data; for example, use of Kaplan–Meier curves looking at time to withdrawal. Copyright © 2009 John Wiley & Sons, Ltd.     

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived "factors" as representing biological "subpathway" structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology.     

Levels of urbanization in the world’s countries: testing consistency of estimates based on national definitions

Estimates of urbanization levels for all countries, as published in the United Nations World Urbanization Prospects, are not comparable across countries because they are based on national definitions of urbanization, which vary greatly across countries. We construct alternative estimates of urbanization that are comparable across countries, to use these estimates to point out definitional problems or outlying countries with interesting urban characteristics. The alternative estimates are constructed using variables that are associated with urbanization, as well as a categorical variable that summarizes the urban definition used in the country. We find that among the 181 countries included in the analysis, 21 % have a restrictive definition, and 31 % a generous definition of urbanization. In 2000, the difference between the alternative estimate and the UN estimate was more than 10 % for 67 countries. An examination of six countries with surprisingly low or high reported levels of urbanization, as compared the alternative estimates, highlights issues with the plausibility of the official estimate. We conclude that comparison of urbanization estimates across countries should be treated with care, given the wide range of definitions used. To produce more informative estimates of urbanization, several countries would need to clarify or rethink their definition of urban.