The metafor package provides functions for conducting meta-analyses in R. The package includes functions for fitting the meta-analytic fixed-and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. Meta-regression analyses with continuous and categorical moderators can be conducted in this way. Functions for the Mantel-Haenszel and Peto's one-step method for metaanalyses of 2 × 2 table data are also available. Finally, the package provides various plot functions (for example, for forest, funnel, and radial plots) and functions for assessing the model fit, for obtaining case diagnostics, and for tests of publication bias.

The present study used meta-analytic techniques (number of samples = 92) to determine the patterns of mean-level change in personality traits across the life course. Results showed that people increase in measures of social dominance (a facet of extraversion), conscientiousness, and emotional stability, especially in young adulthood (age 20 to 40). In contrast, people increase on measures of social vitality (a 2nd facet of extraversion) and openness in adolescence but then decrease in both of these domains in old age. Agreeableness changed only in old age. Of the 6 trait categories, 4 demonstrated significant change in middle and old age. Gender and attrition had minimal effects on change, whereas longer studies and studies based on younger cohorts showed greater change.

Evidence suggests that adverse experiences in childhood are associated with psychosis. To examine the association between childhood adversity and trauma (sexual abuse, physical abuse, emotional/psychological abuse, neglect, parental death, and bullying) and psychosis outcome, MEDLINE, EMBASE, PsychINFO, and Web of Science were searched from January 1980 through November 2011. We included prospective cohort studies, large-scale cross-sectional studies investigating the association between childhood adversity and psychotic symptoms or illness, case-control studies comparing the prevalence of adverse events between psychotic patients and controls using dichotomous or continuous measures, and case-control studies comparing the prevalence of psychotic symptoms between exposed and nonexposed subjects using dichotomous or continuous measures of adversity and psychosis. The analysis included 18 case-control studies (n = 2048 psychotic patients and 1856 nonpsychiatric controls), 10 prospective and quasi-prospective studies (n = 41 803) and 8 population-based cross-sectional studies (n = 35 546). There were significant associations between adversity and psychosis across all research designs, with an overall effect of OR = 2.78 (95% CI = 2.34–3.31). The integration of the case-control studies indicated that patients with psychosis were 2.72 times more likely to have been exposed to childhood adversity than controls (95% CI = 1.90–3.88). The association between childhood adversity and psychosis was also significant in population-based cross-sectional studies (OR = 2.99 [95% CI = 2.12–4.20]) as well as in prospective and quasi-prospective studies (OR = 2.75 [95% CI = 2.17–3.47]). The estimated population attributable risk was 33% (16%–47%). These findings indicate that childhood adversity is strongly associated with increased risk for psychosis.

The presence of outliers and influential cases may affect the validity and robustness of the conclusions from a meta-analysis. While researchers generally agree that it is necessary to examine outlier and influential case diagnostics when conducting a meta-analysis, limited studies have addressed how to obtain such diagnostic measures in the context of a meta-analysis. The present paper extends standard diagnostic procedures developed for linear regression analyses to the meta-analytic fixed- and random/mixed-effects models. Three examples are used to illustrate the usefulness of these procedures in various research settings. Issues related to these diagnostic procedures in meta-analysis are also discussed. Copyright © 2010 John Wiley & Sons, Ltd.

The current systematic review and meta-analysis provides an extended and comprehensive overview of the associations between neurocognitive and social cognitive functioning and different types of functional outcome. Literature searches were conducted in MEDLINE and PsycINFO and reference lists from identified articles to retrieve relevant studies on cross-sectional associations between neurocognition, social cognition and functional outcome in individuals with non-affective psychosis. Of 285 studies identified, 52 studies comprising 2692 subjects met all inclusion criteria. Pearson correlations between cognition and outcome, demographic data, sample sizes and potential moderator variables were extracted. Forty-eight independent meta-analyses, on associations between 12 a priori identified neurocognitive and social cognitive domains and 4 domains of functional outcome yielded a number of 25 significant mean correlations. Overall, social cognition was more strongly associated with community functioning than neurocognition, with the strongest associations being between theory of mind and functional outcomes. However, as three-quarters of variance in outcome were left unexplained, cognitive remediation approaches need to be combined with therapies targeting other factors impacting on outcome.

Meta‐analyses are typically used to estimate the overall/mean of an outcome of interest. However, inference about between‐study variability, which is typically modelled using a between‐study variance parameter, is usually an additional aim. The DerSimonian and Laird method, currently widely used by default to estimate the between‐study variance, has been long challenged. Our aim is to identify known methods for estimation of the between‐study variance and its corresponding uncertainty, and to summarise the simulation and empirical evidence that compares them. We identified 16 estimators for the between‐study variance, seven methods to calculate confidence intervals, and several comparative studies. Simulation studies suggest that for both dichotomous and continuous data the estimator proposed by Paule and Mandel and for continuous data the restricted maximum likelihood estimator are better alternatives to estimate the between‐study variance. Based on the scenarios and results presented in the published studies, we recommend the Q‐profile method and the alternative approach based on a ‘generalised Cochran between‐study variance statistic’ to compute corresponding confidence intervals around the resulting estimates. Our recommendations are based on a qualitative evaluation of the existing literature and expert consensus. Evidence‐based recommendations require an extensive simulation study where all methods would be compared under the same scenarios. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.

The meta-analytic random effects model assumes that the variability in effect size estimates drawn from a set of studies can be decomposed into two parts: heterogeneity due to random population effects and sampling variance. In this context, the usual goal is to estimate the central tendency and the amount of heterogeneity in the population effect sizes. The amount of heterogeneity in a set of effect sizes has implications regarding the interpretation of the meta-analytic findings and often serves as an indicator for the presence of potential moderator variables. Five population heterogeneity estimators were compared in this article analytically and via Monte Carlo simulations with respect to their bias and efficiency.

About 17% of humanity goes through an episode of major depression at some point in their lifetime. Despite the enormous societal costs of this incapacitating disorder, it is largely unknown how the likelihood of falling into a depressive episode can be assessed. Here, we show for a large group of healthy individuals and patients that the probability of an upcoming shift between a depressed and a normal state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions. These are indicators of the general phenomenon of critical slowing down, which is expected to occur when a system approaches a tipping point. Our results support the hypothesis that mood may have alternative stable states separated by tipping points, and suggest an approach for assessing the likelihood of transitions into and out of depression. D epression is one of the main mental health hazards of our time. It can be viewed as a continuum with an absence of depressive symptoms at the low endpoint and severe and debilitating complaints at the high end (1). (Throughout this manuscript, the term "depression" refers to this continuum of depressive symptoms.) The diagnosis major depressive disorder (MDD) defines individuals at the high end of this continuum. Approximately 10-20% (2) of the general population will experience at least one episode of MDD during their lives, but even subclinical levels of depression may considerably reduce quality of life and work productivity (3). Depressive symptoms are therefore associated with substantial personal and societal costs (4,5). The onset of MDD in an individual can be quite abrupt, and similarly rapid shifts from depression into a remitted state, so-called sudden gains, are common (6). However, despite the high prevalence and associated societal costs of depression, we have little insight into how such critical transitions from health to depression (and vice versa) in individuals might be foreseen. Traditionally, the broad array of correlated symptoms found in depressed people (e.g., depressed mood, insomnia, fatigue, concentration problems, loss of interest, suicidal ideation, etc.) was thought to stem from some common cause, much as a lung tumor is the common cause of symptoms such as shortness of breath, chest pain, and coughing up blood. Recently, however, this common-cause view has been challenged (7-9). The alternative view is that the correlated symptoms should be regarded as the result of interactions of components of a complex dynamical system (7,(10)(11)(12). Consequently, new models of the etiology of depression involve a network of interactions between components, such as emotions, cognitions, and behaviors (8,9). This implies, for instance, that a person may become depressed through a causal chain of feelings and experiences, such as the following: stress → negative emotions → sleep problems → anhedonia (9, 13-15). However, the network view also implies that there can be positive feedback mechanisms between symptoms, such...