This systematic review and meta-analysis examined 27 studies published between 2003 and 2024 to assess the prevalence of Fasciola hepatica infestation in various animal species in Algeria. Diagnostic methods included liver inspection (16 studies), ELISA (7 studies), coproscopy (4 studies), bile microscopy (1 study), and abattoir data analysis (1 study). For humans, coproscopy and immunoelectrophoresis (IEP) were used in one study in Algiers. Among the 1,006,751 animals examined, 15,868 tested positive, resulting in an overall prevalence of 1.57% (CI 1.55–1.59). Prevalence was higher in the northeastern regions of Algeria (El Tarf, Annaba, and Jijel) at 15.95%, compared to other regions (0.9%–2.95%) (p<0.0001). Cattle showed the highest prevalence (3.91%; CI 3.84–3.98) (p<0.001), followed by sheep (0.42%; CI 0.40–0.44) and goats (0.12%; CI 0.10–0.14). Camels had a prevalence rate of 4%. Trend analysis over 20 years indicated a progressive decrease in prevalence, from 13.29% (2004–2009) to 1.79% (2010–2019) and 1.12% (2020–2024) (p<0.0001). The ELISA method was found to be the most sensitive, revealing a prevalence of 16.40% (CI 15.23–17.57) (true adjusted prevalence is 12.38%) (p<0.0001), significantly higher than liver inspection (1.83%), coproscopy (1.04%), and abattoir data analysis (1.10%). Prevalence increased with animal age across all species. This study clearly shows that fasciolosis in Algeria is most prevalent in the northeast region and that cattle are the high-risk group of animals. As a result, control strategies are urgently needed, targeting cattle in particular in northeast Algeria, to prevent and control this disease and thus reduce Fasciola infection.

Growing evidence highlights the critical role of patient choice of treatment, with significant benefits for outcomes found in some studies. While four meta-analyses have previously examined the association between treatment choice and outcomes in mental health, robust conclusions have been limited by the inclusion of studies with biased preference trial designs. The current systematic review included 30 studies across three common and frequently comorbid mental health disorders (depression N = 23; anxiety, N = 5; eating disorders, N = 2) including 7055 participants (Mage 42.5 years, SD 11.7; 69.5% female). Treatment choice most often occurred between psychotherapy and antidepressant medication (43.3%), followed by choice between two different forms of psychotherapy, or elements within psychotherapy (36.7%). There were insufficient studies with stringent designs to conduct meta-analyses for anxiety or eating disorders as outcomes, or for treatment uptake. Treatment choice significantly improved outcomes for depression (d = 0.17, n = 18) and decreased therapy dropout, both in a combined sample targeting depression (n = 12), anxiety (n = 4) and eating disorders (n = 1; OR = 1.46, 95% CI: 1.17, 1.83), and in a smaller sample of the depression studies alone (OR = 1.65, 95% CI: 1.05, 2.59). All studies evaluated the impact of adults making treatment choices with none examining the effect of choice in adolescents. Clear directions in future research are indicated, in terms of designing studies that can adequately test the treatment choice and outcome association in anxiety and eating disorder treatment, and in youth.

Repetitive negative thinking (RNT) is a transdiagnostic process associated with the onset, maintenance, and risk of relapse of various mental disorders. However, previous research syntheses addressing the effect of cognitive behavioral therapy (CBT) on RNT are limited to specific diagnoses, treatments, or RNT constructs (transdiagnostic RNT, worry, rumination). In the present meta-analysis, we integrate findings from randomized controlled trials (RCTs) of CBT on RNT across diagnoses, intervention types, and RNT constructs. We investigate the following questions: What is the overall transdiagnostic efficacy of CBT interventions on all post-treatment RNT outcomes? Which RNT construct is addressed most effectively? Are RNT-specific treatments superior in reducing RNT than less specific approaches? Inclusion criteria were met by 55 studies with a total of 4,970 participants. The overall post-treatment effect of CBT interventions on RNT compared to respective control groups was moderate in favor of CBT (g = −0.67). Treatment efficacy did not differ significantly by RNT construct. RNT-specific interventions (g = −0.99) were significantly more efficacious in reducing RNT than less specific approaches (g = −0.56). Treatment efficacy was not significantly enhanced by individual or in-person settings. Our results advocate a dissemination of RNT-specific treatments in research and practice and a general improvement of CBT treatments by focusing on relevant transdiagnostic processes such as RNT.

Previous studies have assessed how supplementing with Garcinia cambogia affects glycaemic control and liver enzyme levels; nevertheless, the results were not consistent. The study aimed to evaluate the impact of Garcinia cambogia on glycaemic control and liver enzymes through a systematic review and meta-analysis. Searches were conducted from the beginning through February 2023, using online databases (Scopus, Web of Science, PubMed, and Cochrane Library). Trials examining the impact of Garcinia cambogia on serum levels of fasting blood sugar (FBS), serum level of insulin, alanine transaminase (ALT), and aspartate transaminase (AST) in adults were included. The overall estimates and their 95% confidence intervals (CIs) were calculated using a random-effects model. This meta-analysis includes nine publications with 444 participants. The results showed that Garcinia cambogia has no significant effect on FBS (weighted mean difference (WMD): 1.02 mg/dl, 95% CI: −1.29, 3.33), insulin (WMD: −0.12 mU/L, 95% CI: −1.50, 1.25), AST (Hedges’ g: −0.08, 95% CI: −0.43, 0.26), and ALT (Hedges’ g: 0.27, 95% CI: −0.20, 0.73). Subgroup analysis showed that Garcinia cambogia significantly increased insulin levels in females and also increased insulin and FBS levels in those with a BMI ≥30 kg/m2. Nevertheless, the administration of Garcinia cambogia for more than 8 weeks significantly decreased insulin levels. This meta-analysis showed that supplementation with Garcinia cambogia has no significant effect on FBS, insulin, ALT, or AST levels compared with control groups; however, it seems that increasing the duration of the intervention may have a decreasing effect on insulin levels.

In a seminal paper, Frederick et al. (J Consum Res 36:553–561, 2009) showed that people’s willingness to purchase a consumer good declined dramatically when opportunity costs were made more salient (Cohen’s d = 0.45–0.85). This finding suggests that people normally do not pay sufficient attention to opportunity costs and as a result make poorer and less efficient decisions, both in private and public domains. To critically assess the strength of opportunity cost neglect, we carried out a systematic review and a meta-analysis including published and non-published experimental work. In total, 39 experimental studies were included in the meta-analysis (N = 14,005). The analysis shows a robust significant effect (Cohen’s d = 0.22; p < 0.001) of opportunity cost neglect across different domains, albeit the effect is considerably smaller than what was originally estimated by Frederick et al. (2009). Our findings highlight the importance of meta-analyses and replications of initial findings.

We test whether anchoring affects people’s elicited valuations for a bottle of wine in individual decision-making and in markets. We anchor subjects by asking them if they are willing to sell a bottle of wine for a transparently uninformative random price. We elicit subjects’ Willingness-To-Accept for the bottle before and after the market. Subjects participate in a double auction market either in a small or a large trading group. The variance in subjects’ Willingness-To-Accept shrinks within trading groups. Our evidence supports the idea that markets have the potential to diminish anchoring effects. However, the market is not needed: our anchoring manipulation failed in a large sample. In a concise meta-analysis, we identify the circumstances under which anchoring effects of preferences can be expected.

We perform a meta analysis of gender differences in the standard windfall gains dictator game (DG) by collecting raw data from 53 studies with 117 conditions, giving us 15,016 unique individual observations. We find that women on average give 4 percentage points more than men (Cohen’s $d=0.16$), and that this difference decreases to $3.1\%$ points (Cohen’s $d=0.13$) if we exclude studies where dictators can only give all or nothing. The gender difference is larger if the recipient in the DG is a charity, compared to the standard DG with an anonymous individual as the recipient (a 10.9 versus a $2.3\%$ points gender difference). These effect sizes imply that many individual studies on gender differences are underpowered; the median power in our sample of standard DG studies is only $9\%$ to detect the meta-analytic gender difference at the $5\%$ significance level. Moving forward on this topic, sample sizes should thus be substantially larger than what has been the norm in the past.

A growing body of research has found that talking to young children is positively associated with language outcomes. However, there is tremendous heterogeneity in the design of these studies, which could potentially affect the strength and reliability of this association. The present meta-analysis, comprising 4760 participants across 71 studies, goes beyond prior research by including: 1) more recent studies, 2) non-English-speaking populations, 3) more fine-grained categorization of measures of input, 4) additional moderators, and 5) a multilevel model design allowing us to consider multiple effect sizes per study. We find a moderate association between input and outcomes (R2=0.04-0.07) across four input measures, with some evidence of publication bias. We find no differences in effect size across any of the input measures. Child age and study duration moderated some effects of input. Our findings suggest that language input-outcome associations remain robust but modest across a multitude of contexts and measures.

A maximum likelihood procedure for combining standardized mean differences based on a noncentratt-distribution is proposed. With a proper data augmentation technique, an EM-algorithm is developed. Information and likelihood ratio statistics are discussed in detail for reliable inference. Simulation results favor the proposed procedure over both the existing normal theory maximum likelihood procedure and the commonly used generalized least squares procedure.

A growing number of publications focus on estimating Gaussian graphical models (GGM, networks of partial correlation coefficients). At the same time, generalizibility and replicability of these highly parameterized models are debated, and sample sizes typically found in datasets may not be sufficient for estimating the underlying network structure. In addition, while recent work emerged that aims to compare networks based on different samples, these studies do not take potential cross-study heterogeneity into account. To this end, this paper introduces methods for estimating GGMs by aggregating over multiple datasets. We first introduce a general maximum likelihood estimation modeling framework in which all discussed models are embedded. This modeling framework is subsequently used to introduce meta-analytic Gaussian network aggregation (MAGNA). We discuss two variants: fixed-effects MAGNA, in which heterogeneity across studies is not taken into account, and random-effects MAGNA, which models sample correlations and takes heterogeneity into account. We assess the performance of MAGNA in large-scale simulation studies. Finally, we exemplify the method using four datasets of post-traumatic stress disorder (PTSD) symptoms, and summarize findings from a larger meta-analysis of PTSD symptom.

Estimation of effect size is of interest in many applied fields such as Psychology, Sociology and Education. However there are few nonparametric estimators of effect size proposed in the existing literature, and little is known about the distributional characteristics of these estimators. In this article, two estimators based on the sample quantiles are proposed and studied. The first one is the estimator suggested by Hedges and Olkin (see page 93 of Hedges & Olkin, 1985) for the situation where a treatment effect is evaluated against a control group (Case A). A modified version of the robust estimator by Hedges and Olkin is also proposed for the situation where two parallel treatments are compared (Case B). Large sample distributions of both estimators are derived. Their asymptotic relative efficiencies with respect to the normal maximum likelihood estimators under several common distributions are evaluated. The robust properties of the proposed estimators are discussed with respect to the sample-wise breakdown points proposed by Akritas (1991). Simulation studies are provided in which the performing characteristics of the proposed estimator are compared to that of the nonparametric estimators by Kraemer and Andrews (1982). Interval estimation of the effect sizes is also discussed. In an example, interval estimates for the data set in Kraemer and Andrews (1982) are calculated for both cases A and B.

We introduce multilevel multivariate meta-analysis methodology designed to account for the complexity of contemporary psychological research data. Our methodology directly models the observations from a set of studies in a manner that accounts for the variation and covariation induced by the facts that observations differ in their dependent measures and moderators and are nested within, for example, papers, studies, groups of subjects, and study conditions. Our methodology is motivated by data from papers and studies of the choice overload hypothesis. It more fully accounts for the complexity of choice overload data relative to two prior meta-analyses and thus provides richer insight. In particular, it shows that choice overload varies substantially as a function of the six dependent measures and four moderators examined in the domain and that there are potentially interesting and theoretically important interactions among them. It also shows that the various dependent measures have differing levels of variation and that levels up to and including the highest (i.e., the fifth, or paper, level) are necessary to capture the variation and covariation induced by the nesting structure. Our results have substantial implications for future studies of choice overload.

The paper outlines several approaches for dealing with meta-analyses of count outcome data. These counts are the accumulation of occurred events, and these events might be rare, so a special feature of the meta-analysis is dealing with low counts including zero-count studies. Emphasis is put on approaches which are state of the art for count data modelling including mixed log-linear (Poisson) and mixed logistic (binomial) regression as well as nonparametric mixture models for count data of Poisson and binomial type. A simulation study investigates the performance and capability of discrete mixture models in estimating effect heterogeneity. The approaches are exemplified on a meta-analytic case study investigating the acceptance of bibliotherapy.

We construct a framework for meta-analysis and other multi-level data structures that codifies the sources of heterogeneity between studies or settings in treatment effects and examines their implications for analyses. The key idea is to consider, for each of the treatments under investigation, the subject’s potential outcome in each study or setting were he to receive that treatment. We consider four sources of heterogeneity: (1) response inconsistency, whereby a subject’s response to a given treatment would vary across different studies or settings, (2) the grouping of nonequivalent treatments, where two or more treatments are grouped and treated as a single treatment under the incorrect assumption that a subject’s responses to the different treatments would be identical, (3) nonignorable treatment assignment, and (4) response-related variability in the composition of subjects in different studies or settings. We then examine how these sources affect heterogeneity/homogeneity of conditional and unconditional treatment effects. To illustrate the utility of our approach, we re-analyze individual participant data from 29 randomized placebo-controlled studies on the cardiovascular risk of Vioxx, a Cox-2 selective nonsteroidal anti-inflammatory drug approved by the FDA in 1999 for the management of pain and withdrawn from the market in 2004.

The use of p-values in combining the results of independent studies often involves studies that are potentially aberrant either in quality or in actual values. A robust data analysis suggests the use of a statistic that takes these aberrations into account by trimming some of the largest and smallest p-values. We present a trimmed statistic based on an inverse cumulative normal transformation of the ordered p-values, together with a simple and convenient method for approximating the distribution and first two moments of this statistic.