Researchers would often like to leverage data from a collection of sources (e.g., meta-analyses of randomized trials, multi-center trials, pooled analyses of observational cohorts) to estimate causal effects in a target population of interest. However, because different data sources typically represent different underlying populations, traditional meta-analytic methods may not produce causally interpretable estimates that apply to any reasonable target population. In this article, we present the CausalMetaR R package, which implements robust and efficient methods to estimate causal effects in a given internal or external target population using multi-source data. The package includes estimators of average and subgroup treatment effects for the entire target population. To produce efficient and robust estimates of causal effects, the package implements doubly robust and non-parametric efficient estimators and supports using flexible data-adaptive (e.g., machine learning techniques) methods and cross-fitting techniques to estimate the nuisance models (e.g., the treatment model, the outcome model). We briefly review the methods, describe the key features of the package, and demonstrate how to use the package through an example. The package aims to facilitate causal analyses in the context of meta-analysis.

In small meta-analyses (e.g., up to 20 studies), the best-performing frequentist methods can yield very wide confidence intervals for the meta-analytic mean, as well as biased and imprecise estimates of the heterogeneity. We investigate the frequentist performance of alternative Bayesian methods that use the invariant Jeffreys prior. This prior has the usual Bayesian motivation, but also has a purely frequentist motivation: the resulting posterior modes correspond to the established Firth bias correction of the maximum likelihood estimator. We consider two forms of the Jeffreys prior for random-effects meta-analysis: the previously established “Jeffreys1” prior treats the heterogeneity as a nuisance parameter, whereas the “Jeffreys2” prior treats both the mean and the heterogeneity as estimands of interest. In a large simulation study, we assess the performance of both Jeffreys priors, considering different types of Bayesian estimates and intervals. We assess point and interval estimation for both the mean and the heterogeneity parameters, comparing to the best-performing frequentist methods. For small meta-analyses of binary outcomes, the Jeffreys2 prior may offer advantages over standard frequentist methods for point and interval estimation of the mean parameter. In these cases, Jeffreys2 can substantially improve efficiency while more often showing nominal frequentist coverage. However, for small meta-analyses of continuous outcomes, standard frequentist methods seem to remain the best choices. The best-performing method for estimating the heterogeneity varied according to the heterogeneity itself. Röver & Friede’s R package bayesmeta implements both Jeffreys priors. We also generalize the Jeffreys2 prior to the case of meta-regression.

Results of meta-analyses are of interest not only to researchers but often to policy-makers and other decision-makers (e.g., in education and medicine), and visualizations play an important role in communicating data and statistical evidence to the broader public. Therefore, the potential audience of meta-analytic visualizations is broad. However, the most common meta-analytic visualization – the forest plot – uses non-optimal design principles that do not align with data visualization best practices and relies on statistical knowledge and conventions not likely to be familiar to a broad audience. Previously, the Meta-Analytic Rain Cloud (MARC) plot has been shown to be an effective alternative to a forest plot when communicating the results of a small meta-analysis to education practitioners. However, the original MARC plot design was not well-suited for meta-analyses with large numbers of effect sizes as is common across the social sciences. This paper presents an extension of the MARC plot, intended for effective communication of moderate to large meta-analyses (k = 10, 20, 50, 100 studies). We discuss the design principles of the MARC plot, grounded in the data visualization and cognitive science literature. We then present the methods and results of a randomized survey experiment to evaluate the revised MARC plot in comparison to the original MARC plot, the forest plot, and a bar plot. We find that the revised MARC plot is more effective for communicating moderate to large meta-analyses to non-research audiences, offering a 0.30, 0.34, and 1.07 standard deviation improvement in chart users’ scores compared to the original MARC plot, forest plot, and bar plot, respectively.

Cannabis use is highly prevalent in people with schizophrenia and is related to adverse clinical outcomes, including relapse and hospitalization. However, the relationship between cannabis and suicide remains inconclusive. This study aimed to systematically review and meta-analyze the relationship between cannabis use and suicide-related outcomes in people with schizophrenia. A comprehensive search of Medline, Embase, and PsycINFO for cross-sectional, case-control, and longitudinal studies was conducted using search terms from database inception to November 2024 inclusive. Computation of odds ratios (ORs) and hazard ratios (HRs) was performed using random effects models with DerSimonian-Laird estimation. All studies were appraised for quality. We also evaluated heterogeneity, publication bias and performed sub-group analyses and meta-regression. Twenty-nine studies comprising 36 samples met eligibility criteria. Cannabis use was not associated with odds of suicide death or suicidal ideation but was associated with risks of suicide death (HR = 1.21, 95% CI = 1.04 – 1.40) and odds of attempted suicide (OR = 1.40, 95% CI = 1.16 – 1.68). While between-sample heterogeneity was moderate in analyses of attempted suicide (I2 = 39.6%, p = 0.03), there was no publication bias. Summary effects remained significant in most sub-groups, but just failed to reach significance in longitudinal studies of attempted suicide (OR = 1.40, 95% CI = 0.97 – 1.68) and studies investigating first episode samples (OR = 1.24, 95% CI = 0.99 – 1.55). Cannabis use is significantly associated with some, but not all, suicide-related outcomes in people with schizophrenia. More work is needed to examine potential mechanisms of significant relationships.

The number of published systematic reviews has increased over the last years, with a non-negligible proportion displaying methodological concerns. We aimed to develop and evaluate a tool to assess the reported methodological quality of medical systematic reviews. The developed tool (ReMarQ) consists of 26 dichotomous items. We applied an item response theory model to assess the difficulty and discrimination of the items and decision tree models to identify those items more capable of identifying systematic reviews with higher reported methodological quality. ReMarQ was applied to a representative sample of medical systematic reviews (excluding those published in the Cochrane Database of Systematic Reviews) to describe their methodological quality and identify associated factors. We assessed 400 systematic reviews published between 2010 and 2020, of which 196 (49.0%) included meta-analysis. The most discriminative items were (i) conducting a risk of bias assessment, (ii) having a published protocol and (iii) reporting methods for solving disagreements. More recent systematic reviews (adjusted yearly RR=1.03; 95%CI=1.02 −1.04, p<0.001) and those with meta-analysis (adjusted RR=1.34; 95%CI=1.25 −1.43, p<0.001) were associated with higher reported methodological quality. Such an association was not observed with the journal impact factor. The items most frequently fulfilled were (i) reporting search dates, (ii) reporting bibliographic sources and (iii) searching multiple electronic bibliographic databases. ReMarQ, consisting of dichotomous items and whose application does not require subject content expertise, may be important (i) in supporting an efficient quality assessment of systematic reviews and (ii) as the basis of automated processes to support that assessment.

Prior studies have shown that plant-based diets are associated with lower cardiovascular risk. However, these diets encompass a large diversity of foods with contrasted nutritional quality that may differentially impact health. We aimed to investigate the pooled cross-sectional association between metabolic syndrome (MetS), its components, and healthy and unhealthy plant-based diet indices (hPDI and uPDI), using data from two French cohorts and one representative study from the French population. This study included 16,358 participants from the NutriNet-Santé study, 1,769 participants from the Esteban study and 1,565 participants from the STANISLAS study who underwent a clinical visit. The MetS was defined according to the International Diabetes Federation definition. The associations between these plant-based diet indices and MetS were estimated by multivariable Poisson and logistic regression models, stratified by gender. Meta-analysis enabled the computation of a pooled Prevalence Ratio. A higher contribution of healthy plant foods (higher hPDI) was associated with a lower probability of having MetS (PRmen: 0.85; 95% CI: 0.75–0.94, PRwomen: 0.72; 95% CI: 0.67–0.77), elevated waist circumferences and elevated blood pressure. In women, a higher hPDI was associated with a lower probability of having elevated triglycerides, low HDL-cholesterolemia and hyperglycemia; and a higher contribution of unhealthy plant foods was associated with a higher prevalence of MetS (PRwomen: 1.13; 95% CI: 1.01–1.26) and elevated triglycerides. A greater contribution of healthy plant floods was associated with protective effects on metabolic syndrome, especially in women. Gender differences should be further investigated in relation to the current sustainable nutrition transition.

To optimize the antidepressant efficacy of repetitive transcranial magnetic stimulation (rTMS), it is important to examine the impact of brain state during therapeutic rTMS. Evidence suggests that brain state can modulate the brain’s response to stimulation, potentially diminishing antidepressant efficacy if left uncontrolled or enhancing it with inexpensive psychological or other non-pharmacological methods. Thus, we conducted a PRISMA-ScR-based scoping review to pool studies administering rTMS with psychological and other non-pharmacological methods. PubMed and Web of Science databases were searched from inception to 10 July 2024. Inclusion criteria: neuropsychiatric patients underwent rTMS; studies assessed depressive symptom severity; non-pharmacological tasks or interventions were administered during rTMS, or did not include a wash-out period. Of 8,442 studies, 20 combined rTMS with aerobic exercise, bright light therapy, cognitive training or reactivation, psychotherapy, sleep deprivation, or a psychophysical task. Meta-analyses using random effects models were conducted based on change scores on standardized scales. The effect size was large and therapeutic for uncontrolled pretest-posttest comparisons (17 studies, Hedges’ g = −1.91, (standard error) SE = 0.45, 95% (confidence interval) CI = −2.80 to −1.03, p < 0.01); medium when studies compared active combinations with sham rTMS plus active non-pharmacological methods (8 studies, g = −0.55, SE = 0.14, 95% CI = −0.82 to −0.28, p < 0.01); and non-significant when active combinations were compared with active rTMS plus sham psychological methods (4 studies, p = 0.96). Attempts to administer rTMS with non-pharmacological methods show promise but have not yet outperformed rTMS alone.

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.