Prediction based classification for longitudinal biomarkers

Assessment of circulating CD4 count change over time in HIV-infected subjects on antiretroviral therapy (ART) is a central component of disease monitoring. The increasing number of HIV-infected subjects starting therapy and the limited capacity to support CD4 count testing within resource-limited settings have fueled interest in identifying correlates of CD4 count change such as total lymphocyte count, among others. The application of modeling techniques will be essential to this endeavor due to the typically non-linear CD4 trajectory over time and the multiple input variables necessary for capturing CD4 variability. We propose a prediction based classification approach that involves first stage modeling and subsequent classification based on clinically meaningful thresholds. This approach draws on existing analytical methods described in the receiver operating characteristic curve literature while presenting an extension for handling a continuous outcome. Application of this method to an independent test sample results in greater than 98% positive predictive value for CD4 count change. The prediction algorithm is derived based on a cohort of n = 270 HIV-1 infected individuals from the Royal Free Hospital, London who were followed for up to three years from initiation of ART. A test sample comprised of n = 72 individuals from Philadelphia and followed for a similar length of time is used for validation. Results suggest that this approach may be a useful tool for prioritizing limited laboratory resources for CD4 testing after subjects start antiretroviral therapy.     

Objective Testing Procedures in Linear Models: Calibration of the p-values

Abstract.  An optimal Bayesian decision procedure for testing hypothesis in normal linear models based on intrinsic model posterior probabilities is considered. It is proven that these posterior probabilities are simple functions of the classical F -statistic, thus the evaluation of the procedure can be carried out analytically through the frequentist analysis of the posterior probability of the null. An asymptotic analysis proves that, under mild conditions on the design matrix, the procedure is consistent. For any testing hypothesis it is also seen that there is a one-to-one mapping – which we call calibration curve – between the posterior probability of the null hypothesis and the classical bi p -value. This curve adds substantial knowledge about the possible discrepancies between the Bayesian and the p -value measures of evidence for testing hypothesis. It permits a better understanding of the serious difficulties that are encountered in linear models for interpreting the p -values. A specific illustration of the variable selection problem is given.     

Poverty Catchments: Migration,Residential Mobility,and Population Turnover in Impoverished Rural Illinois Communities

Abstract Research has thoroughly documented how out‐migration of the educated and skilled from rural areas leaves behind a poorer population and creates pockets of rural poverty. Recently, studies have recognized that the poor are also geographically mobile and that poverty migration patterns can reinforce rural poverty concentrations. In this process, certain impoverished rural communities in economically depressed regions receive a disproportionate share of poverty migrants, concentrating poverty in certain locations. This paper examines the conditions and processes through which poor rural communities become likely destinations for a highly mobile segment of the rural poor and near‐poor. Utilizing case studies of depressed rural Illinois communities, it investigates how the interplay of community factors and the behavior of migrants transforms rural communities from residentially stable to highly mobile, impoverished places.     

The Impact of Migration on Poverty Concentrations in the United States, 1995–2000

Poverty is frequently conceptualized as an attribute of either people or places. Yet residential movement of poor people can redistribute poverty across places, affecting and reshaping the spatial concentration of economic disadvantage. In this article, we utilize 1995 to 2000 county‐to‐county migration data from the 2000 United States decennial census to explore how differential migration rates of the poor and nonpoor affect local incidence of poverty, and how migration reconfigures poverty rates across metropolitan, micropolitan, and noncore counties. We further examine the impact of differential migration rates on African American and Latino poverty rates, two groups that have experienced higher than average poverty rates and have a sizable presence in rural areas. Our analysis indicates that during the 1990s the poor moved at rates equal to or greater than the nonpoor, and that, especially in micropolitan counties, this movement tended to deepen existing poverty concentrations. Both African American and Latino migration patterns tended to reinforce existing poverty concentrations, a result similar to that of the population as a whole, although the migration patterns of both groups more severely exacerbated poverty in high‐poverty noncore counties.     

Long distance intimacy: class,gender and intergenerational relations between mothers and children in Filipino transnational families

Abstract In this article I address transnational intergenerational relations between Filipino migrant mothers and their young adult children and examine how families achieve intimacy across great distances. I do this by identifying and examining the transnational communication methods Filipino migrant families use to develop intimacy, in other words familiarity, across borders. In my analysis, I address how political economy and gender shape the dynamics of transnational communication. By showing how economic conditions and gender shape transnational family communication, I provide a socially thick lens through which to understand the formation of transnational intimacy and emphasize how larger systems of inequality shape the lives of the children left behind by the global migration of women.     

Essential elements of implementing a system of concurrent planning

In efforts to preserve foster children’s biological families and to promote the formation of alternative families in some situations, legislative efforts to promote ‘permanency’ have been gaining pace since the late 1990s. One policy and practice tool, concurrent planning (CP), simultaneously allows for the pursuit of reunification and adoption. This article examines six counties in California (USA) and their efforts towards implementation of the components of CP. Researchers used qualitative methods to explore policies and practices of CP. Key informants include social workers, social work supervisors, attorneys and judges. The study identifies and describes seven elements determined to be essential to the complete and successful implementation of CP. Implications for agency practice and family outcomes are discussed.     

Combining genotype groups and recursive partitioning: an application to human immunodeficiency virus type 1 genetics data

Summary.  Understanding the relationship between human immunodeficiency virus type 1 genotypic markers of resistance and response to therapy presents an analytic challenge because of the high dimensionality of the viral genome and the complex interactions across sites on the genome. Recursive partitioning is a natural approach to classifying patients on the basis of covariates that capture information on the variability of a response variable and is therefore well suited to handling both of these features of the problem. In the human immunodeficiency virus genetics setting, we aim to classify patients on the basis of the genotypic characteristics of their infecting viral population so that the resulting groups explain the most variability in drug susceptibility phenotype. Recursive partitioning is also informative in identifying meaningful genotypic patterns that define the resulting classes. By combining dimension reduction techniques with recursive partitioning we can arrive at different classification schemes which are potentially more informative. Furthermore, the use of weighted distance metrics in the clustering algorithm allows us to update and potentially to improve on this classification scheme. An illustration of the relative benefits of the various approaches is provided by using 2559 protease sequences and the corresponding 50% inhibitory concentrations for Indinavir.