The use of power transformations in small area estimation

Sample surveys are usually designed and analysed to produce estimates for larger areas. Nevertheless, sample sizes are often not large enough to give adequate precision for small area estimates of interest. To overcome such difficulties, borrowing strength from related small areas via modelling becomes essential. In line with this, we propose components of variance models with power transformations for small area estimation. This paper reports the results of a study aimed at incorporating the power transformation in small area estimation for improving the quality of small area predictions. The proposed methods are demonstrated on satellite data in conjunction with survey data to estimate mean acreage under a specified crop for counties in Iowa.     

A growth mixture Tobit model: application to AIDS studies

This paper presents an alternative analysis approach to modeling data where a lower detection limit (LOD) and unobserved population heterogeneity exist in a longitudinal data set. Longitudinal data on viral loads in HIV/AIDS studies, for instance, show strong positive skewness and left-censoring. Normalizing such data using a logarithmic transformation seems to be unsuccessful. An alternative to such a transformation is to use a finite mixture model which is suitable for analyzing data which have skewed or multi-modal distributions. There is little work done to simultaneously take into account these features of longitudinal data. This paper develops a growth mixture Tobit model that deals with a LOD and heterogeneity among growth trajectories. The proposed methods are illustrated using simulated and real data from an AIDS clinical study.     

The Congruence Between Adolescents and Their Parents’ Gender-Role Attitudes in Urban Slums of Allahabad,India

Gender role differentiation intensifies during adolescence. The current study explores gender-role attitudes among unmarried young men and women aged 15–21 residing in the slums of Allahabad, India. The survey asked a series of questions about gender role attitudes to young men and women and to their parents (relating to work and educational attainment for girls, marriage and husband–wife relations). Since either the father or mother responded to the parent questionnaire, data analyses are based on subsets of mother–daughter pairs (n = 2124), mother–son pairs (n = 2135), father–son pairs (n = 788), and father–daughter pairs (n = 452). No significant differences emerged in terms of adolescent background characteristics of the four groups and the adolescent population of the study. A fair amount of congruence in gender role attitudes was found in the mother–daughter and father–son pairs for most items. However, a similar pattern was not visible in the father–daughter or mother–son pairs. Multivariate regression analysis indicated strong influence of parental attitudes on the adolescent’s attitudes. While education led to a shift in girls’ attitudes to gender egalitarian ones, it did not impact the attitudes of adolescent boys.     

Bayesian prediction for small areas using sur models

Sample surveys are usually designed and analyzed to produce estimates for larger areas and/or populations. Nevertheless, sample sizes are often not large enough to give adequate precision for small area estimates of interest. To circumvent such difficulties, borrowing strength from related small areas via modeling becomes essential. In line with this, we propose a hierarchical multivariate Bayes prediction method for small area estimation based on the seemingly unrelated regressions (SUR) model. The performance of the proposed method was evaluated through simulation studies.     

Bayesian hierarchical duration model for repeated events: an application to behavioral observations

This article presents a continuous-time Bayesian model for analyzing durations of behavior displays in social interactions. Duration data of social interactions are often complex because of repeated behaviors (events) at individual or group (e.g. dyad) level, multiple behaviors (multistates), and several choices of exit from a current event (competing risks). A multilevel, multistate model is proposed to adequately characterize the behavioral processes. The model incorporates dyad-specific and transition-specific random effects to account for heterogeneity among dyads and interdependence among competing risks. The proposed method is applied to child–parent observational data derived from the School Transitions Project to assess the relation of emotional expression in child–parent interaction to risk for early and persisting child conduct problems.     

Trapped bodies,moving minds: Uncertainty and migration among marginalised urban youth in Ethiopia

This paper from YOUR World Research highlights and explores the role of positive uncertainty in lives, strategies and transitions towards adulthood of marginalised children and youth in two urban locations in Ethiopia. Young men and women embraced notions of positive uncertainty, making journeys to street situations and internationally in search of more hopeful futures. While youth navigate uncertainty positively in the short term, the research suggests this is temporal and their agency relational and embedded. They seek support from peers but still want to belong and be accepted by their families and communities.     

Jointly modeling time-to-event and longitudinal data: a Bayesian approach

This article explores Bayesian joint models of event times and longitudinal measures with an attempt to overcome departures from normality of the longitudinal response, measurement errors, and shortages of confidence in specifying a parametric time-to-event model. We allow the longitudinal response to have a skew distribution in the presence of measurement errors, and assume the time-to-event variable to have a nonparametric prior distribution. Posterior distributions of the parameters are attained simultaneously for inference based on Bayesian approach. An example from a recent AIDS clinical trial illustrates the methodology by jointly modeling the viral dynamics and the time to decrease in CD4/CD8 ratio in the presence of CD4 counts with measurement errors to compare potential models with various scenarios and different distribution specifications. The analysis outcome indicates that the time-varying CD4 covariate is closely related to the first-phase viral decay rate, but the time to CD4/CD8 decrease is not highly associated with either the two viral decay rates or the CD4 changing rate over time. These findings may provide some quantitative guidance to better understand the relationship of the virological and immunological responses to antiretroviral treatments.     

Segmental modeling of changing immunologic response for CD4 data with skewness,missingness and dropout

In clinical practice, the profile of each subject's CD4 response from a longitudinal study may follow a ‘broken stick’ like trajectory, indicating multiple phases of increase and/or decline in response. Such multiple phases (changepoints) may be important indicators to help quantify treatment effect and improve management of patient care. Although it is a common practice to analyze complex AIDS longitudinal data using nonlinear mixed-effects (NLME) or nonparametric mixed-effects (NPME) models in the literature, NLME or NPME models become a challenge to estimate changepoint due to complicated structures of model formulations. In this paper, we propose a changepoint mixed-effects model with random subject-specific parameters, including the changepoint for the analysis of longitudinal CD4 cell counts for HIV infected subjects following highly active antiretroviral treatment. The longitudinal CD4 data in this study may exhibit departures from symmetry, may encounter missing observations due to various reasons, which are likely to be non-ignorable in the sense that missingness may be related to the missing values, and may be censored at the time of the subject going off study-treatment, which is a potentially informative dropout mechanism. Inferential procedures can be complicated dramatically when longitudinal CD4 data with asymmetry (skewness), incompleteness and informative dropout are observed in conjunction with an unknown changepoint. Our objective is to address the simultaneous impact of skewness, missingness and informative censoring by jointly modeling the CD4 response and dropout time processes under a Bayesian framework. The method is illustrated using a real AIDS data set to compare potential models with various scenarios, and some interested results are presented.     

A SEQUENTIAL METHOD OF CONSTRUCTING OPTIMAL BLOCK DESIGNS

An algorithm for the construction of a wide class of block designs including Balanced Incomplete Blocks (BIB) is described. The algorithm which allows the experimenter to give weights for a set of treatment contrasts uses an initial starting design to generate an optimal block design sequentially. The performance of the algorithm is illustrated by examples, and designs constructed by the algorithm compare favourably with designs generated by other methods.     

A multivariate analysis of adverse childhood experiences and health behaviors and outcomes among college students

Objective: This study investigated associations between adverse childhood experiences (ACE) prior to age 18 years and multiple health behaviors (eg, cigarette and other substance use) and outcomes (eg, obesity, depression) for a large college sample. Participants: 2,969 college students from seven universities in the state of Georgia were included in the analysis. Methods: Web-based surveys were completed by students (45–60 minutes) during the spring semester, 2015. Results: Findings indicate that more ACEs are associated with higher levels of depressive symptoms, ADHD symptoms, cigarette use, alcohol use, marijuana use, and BMI, in addition to lower levels of fruit and vegetable intake, and sleep. Conclusion: ACEs may carry forward in the lifespan to influence a range of unhealthy outcomes among college students. College intervention programs may benefit by recognizing the pervasiveness of ACEs and their associations with health behaviors and outcomes, and include interventions across more than one health behavior.