A re-evaluation of random-effects meta-analysis

Summary.  Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of 'set shifting' ability in people with eating disorders.     

Automating and evaluating reversible jump MCMC proposal distributions

The reversible jump Markov chain Monte Carlo (MCMC) sampler (Green in Biometrika 82:711–732, 1995) has become an invaluable device for Bayesian practitioners. However, the primary difficulty with the sampler lies with the efficient construction of transitions between competing models of possibly differing dimensionality and interpretation. We propose the use of a marginal density estimator to construct between-model proposal distributions. This provides both a step towards black-box simulation for reversible jump samplers, and a tool to examine the utility of common between-model mapping strategies. We compare the performance of our approach to well established alternatives in both time series and mixture model examples.     

Transformed generalized linear models

The estimation of data transformation is very useful to yield response variables satisfying closely a normal linear model. Generalized linear models enable the fitting of models to a wide range of data types. These models are based on exponential dispersion models. We propose a new class of transformed generalized linear models to extend the Box and Cox models and the generalized linear models. We use the generalized linear model framework to fit these models and discuss maximum likelihood estimation and inference. We give a simple formula to estimate the parameter that index the transformation of the response variable for a subclass of models. We also give a simple formula to estimate the r$r$th moment of the original dependent variable. We explore the possibility of using these models to time series data to extend the generalized autoregressive moving average models discussed by Benjamin et al. [Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214–223]. The usefulness of these models is illustrated in a simulation study and in applications to three real data sets.     

A new estimation method for Weibull-type tails based on the mean excess function

Studying the right tail of a distribution, one can classify the distributions into three classes based on the extreme value index γ$\gamma$. The class γ>0$\gamma >0$ corresponds to Pareto-type or heavy tailed distributions, while γ<0$\gamma <0$ indicates that the underlying distribution has a finite endpoint. The Weibull-type distributions form an important subgroup within the Gumbel class with γ=0$\gamma =0$. The tail behaviour can then be specified using the Weibull tail index. Classical estimators of this index show severe bias. In this paper we present a new estimation approach based on the mean excess function, which exhibits improved bias and mean squared error. The asserted properties are supported by simulation experiments and asymptotic results. Illustrations with real life data sets are provided.     

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model. The authors would like to thank the editor and referees for their helpful comments. This work was supported by CNPq, Brazil.     

The effects of under‐skilling on need for recovery,losing employment and retirement intentions among older office workers: A prospective cohort study

Based on a sample of older workers from the Maastricht Cohort Study, the authors investigate the prevalence and dynamics of self‐reported under‐skilling and its effects on workers' need for recovery, their risk of losing employment and their retirement intentions over a two‐year period. They find that being under‐skilled is associated both with higher levels of need for recovery over time and with the risk of losing employment, but not with early retirement intentions. To achieve sustainable employment, they argue, the course of under‐skilling should be monitored throughout workers' careers, enabling timely interventions to avoid its negative consequences.     

Characteristics of Couples Who Attend Family Services Counselor Visits in Relationship Education

Case management services for low-income couples receiving relationship education have been required by federal funding agencies. Studies examining relationship education outcomes for low-income couples have yet to address participant characteristics of those who attend case management services (e.g., distressed vs. nondistressed). Thus, this study examined the influence of baseline relationship satisfaction and demographics on attendance at case management visits for 2,628 low-income, partnered participants who attended relationship education. Findings indicated that participants who reported higher baseline relationship distress attended more family services counselor (FSC) visits. However, when we added demographics to the model, relationship distress no longer predicted FSC visits, whereas income served as a predictor for men and women, with those reporting greater monthly income attending more FSC visits.     

The Peasant World in the Discourse of Generational Sorrow

This article spells out the methodological considerations and various procedural and methodological subtleties developed over the author's quarter century of sociological field studies of Russia's peasant worlds. Particular attention is paid to “voices from below”—peasant discursive formats that capture, in natural and undiluted form, the evolution of peasant practices constituting the core of everyday life. In particular, the phenomenon of generational sorrow and a special chronological nostalgia that functions as a “social gyroscope” is identified and discursively captured. This phenomenon is noteworthy and useful for its ability to retain a record of the productively longed for past in a rural community's collective memory and thereby influence new peasant worlds taking shape today.     

Context and Scope of Social Work Interventions in Perinatal Mental Health Settings in India

The perinatal period may not be a happy time for all mothers. During this period, some may develop psychiatric complications, which have both biological and psychosocial etiology, and such illness may affect the whole family. Psychosocial aspects may trigger the illness and act as risk and maintaining factors. Social workers need to intervene in this context for optimal functioning of women with perinatal mental health issues and her family. A biopsychosocial framework may be adopted for social work assessment and intervention. Individual, familial, group, and community level intervention is required to address this complex issue, along with other mental health clinicians.