Wolfgang Viechtbauer

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 meta-analyses of 2 x 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.

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.

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 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 meta-analyses of 2 x 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.