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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.
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 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.
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.
Meta-analysis of diagnostic studies experience the common problem that different studies might not be comparable since they have been using a different cut-off value for the continuous or ordered categorical diagnostic test value defining different regions for which the diagnostic test is defined to be positive. Hence specificities and sensitivities arising from different studies might vary just because the underlying cut-off value had been different. To cope with the cut-off value problem interest is usually directed towards the receiver operating characteristic (ROC) curve which consists of pairs of sensitivities and false-positive rates (1-specificity). In the context of meta-analysis one pair represents one study and the associated diagram is called an SROC curve where the S stands for “summary”. In meta-analysis of diagnostic studies emphasis has traditionally been placed on modelling this SROC curve with the intention of providing a summary measure of the diagnostic accuracy by means of an estimate of the summary ROC curve. Here, we focus instead on finding sub-groups or components in the data representing different diagnostic accuracies. The paper will consider modelling SROC curves with the Lehmann family which is characterised by one parameter only. Each single study can be represented by a specific value of that parameter. Hence we focus on the distribution of these parameter estimates and suggest modelling a potential heterogeneous or cluster structure by a mixture of specifically parameterised normal densities. We point out that this mixture is completely nonparametric and the associated mixture likelihood is well-defined and globally bounded. We use the theory and algorithms of nonparametric mixture likelihood estimation to identify a potential cluster structure in the diagnostic accuracies of the collection of studies to be analysed. Several meta-analytic applications on diagnostic studies, including AUDIT and AUDIT-C for detection of unhealthy alcohol use, the mini-mental state examination for cognitive disorders, as well as diagnostic accuracy inspection data on metal fatigue of aircraft spare parts, are discussed to illustrate the methodology.
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 ), and that this difference decreases to points (Cohen’s ) 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 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 to detect the meta-analytic gender difference at the significance level. Moving forward on this topic, sample sizes should thus be substantially larger than what has been the norm in the past.
This paper presents the first meta-analysis of the ‘Taking Game,’ a variant of the Dictator Game where participants take money from recipients instead of giving. Upon analyzing data from 39 experiments, which include 123 effect sizes and 7262 offers made by dictators, we discovered a significant framing effect: dictators are more generous in the Taking Game than in the Dictator Game (Cohen's d = 0.26, p < 0.0001), leaving approximately 35.5 percent of the stakes to recipients in the former as opposed to 27.5 percent in the latter. The difference is higher when the participants have earned their endowment before sharing or when the recipient is a charity. Consistent with the standard literature on giving, we also find that participants take less from a charity than from a standard recipient, take less when payoffs are hypothetical, or when recipients have previously earned their endowment. We also find that women (non-students) take less than men (students). Finally, it appears that participants from non-OECD countries leave more money to recipients than participants from OECD countries.
When the process of publication favors studies with small p-values, and hence large effect estimates, combined estimates from many studies may be biased. This paper describes a model for estimation of effect size when there is selection based on one-tailed p-values. The model employs the method of maximum likelihood in the context of a mixed (fixed and random) effects general linear model for effect sizes. It offers a test for the presence of publication bias, and corrected estimates of the parameters of the linear model for effect magnitude. The model is illustrated using a well-known data set on the benefits of psychotherapy.
This meta-analytic study aims to assess the relationship between innovation and organizational performance. Examining studies published from 2012 to 2021 using a specific protocol resulted in selecting 180 effect sizes from 143 studies. Comprehensive Meta-Analysis Software (CMA2) (2.2.064) software facilitated data analysis. Findings reveal a positive and significant relationship between innovation and organizational performance. Moderating analysis identifies country, continent, year of publication, and innovation type as moderating variables. Additionally, recent years exhibit a noteworthy convergence in the relationship trend between innovation and organizational performance. Enhancing organizational performance remains a critical concern. The study’s outcomes offer valuable insights for managers, especially in international organizations to improve the planning and management of innovation and performance in their various branches and projects in different continents and countries.
Previous studies have shown that helminth infection protects against the development of diabetes mellitus (DM), possibly related to the hygiene hypothesis. However, studies involving Stronglyoides stercoralis and its possible association with DM are scarce and have shown contradicting results, prompting us to perform this meta-analysis to obtain more precise estimates. Related studies were searched from PubMed, Google Scholar, Science Direct, and Cochrane Library until 1 August 2024. Data on the occurrence of DM in patients positive and negative for S. stercoralis were obtained. All analyses were done using Review Manager 5.4. The initial search yielded a total of 1725 studies, and after thorough screening and exclusion, only five articles involving 2106 participants (536 cases and 1570 controls) were included in the meta-analysis. Heterogeneity was assessed, and outlier studies were excluded using a funnel plot. Results showed a significant association of S. stercoralis infection with DM, suggesting that those with the infection are less likely to develop DM. Overall, the results suggest that S. stercoralis infection may decrease the likelihood of developing DM, potentially supporting the hygiene hypothesis.
Meta-analysis is the quantitative analysis of results of a research literature. Typically, meta-analysis is paired with a systematic review that fully documents the search process, inclusion and exclusion criteria, and study characteristics. A key feature of meta-analysis is the calculation of effect sizes – metric-free indices of study outcome that allow the mathematical combination of effects across studies. The methodological literature on meta-analysis has grown rapidly in recent years, yielding an abundance of resources and sophisticated analytic techniques. These developments are improvements to the field but can also be overwhelming to new aspiring meta-analysts. This chapter therefore aims to demystify some of that complexity, offering conceptual explanations instead of mathematical formulas. We aim to help readers who have not conducted a meta-analysis before to get started, as well as to help those who simply want to be intelligent consumers of published meta-analyses.
While omega-3 polyunsaturated fatty acids (PUFAs) have shown promise as an adjunctive treatment for schizophrenia and other psychotic disorders, the overall consensus about their efficacy across studies is still lacking and findings to date are inconclusive. No clinical trials or systematic reviews have yet examined if omega-3 PUFAs are associated with differential levels of efficacy at various stages of psychosis.
Method
A systematic bibliographic search of randomized double-blind placebo-controlled trials (RCTs) examining the effect of omega-3 PUFAs as a monotherapy or adjunctive therapy versus a control group in adults and children at ultra-high risk (UHR) for psychosis, experiencing a first-episode psychosis (FEP), or diagnosed with an established psychotic disorder was conducted. Participants’ clinical symptoms were evaluated using total and subscale scores on validated psychometric scales.
Results
No beneficial effect of omega-3 PUFAs treatment was found in comparison with that of placebo (G = −0.26, 95% CI −0.55 to 0.03, p = 0.08). Treatment of omega-3 PUFAs did not prove any significant improvement in psychopathology in UHR (G = −0.09, 95% CI −0.45 to 0.27, p = 0.63), FEP (G = −1.20, 95% CI −5.63 to 3.22, p = 0.59), or schizophrenia patients (G = −0.17, 95% CI −0.38 to −0.03, p = 0.10).
Conclusion
These findings confirm previous evidence that disputes the original reported findings of the beneficial effect of omega-3 PUFAs in schizophrenia. Furthermore, accumulative evidence of the use of omega-3 as a preventive treatment option in UHR is not supported, suggesting that the need for future studies in this line of research should not be promoted.
Approximately five million individuals have traumatic injuries annually. Implementing prehospital blood-component transfusion (PHBT), encompassing packed red blood cells (p-RBCs), plasma, or platelets, facilitates early hemostatic volume replacement following trauma. The lack of uniform PHBT guidelines persists, relying on diverse parameters and physician experience.
Aim:
This study aims to evaluate the efficacy of various components of PHBT, including p-RBCs and plasma, on mortality and hematologic-related outcomes in traumatic patients.
Methods:
A comprehensive search strategy was executed to identify pertinent literature comparing the transfusion of p-RBCs, plasma, or a combination of both with standard resuscitation care in traumatized patients. Eligible studies underwent independent screening, and pertinent data were systematically extracted. The analysis employed pooled risk ratios (RR) for dichotomous outcomes and mean differences (MD) for continuous variables, each accompanied by their respective 95% confidence intervals (CI).
Results:
Forty studies were included in the qualitative analysis, while 26 of them were included in the quantitative analysis. Solely P-RBCs alone or combined with plasma showed no substantial effect on 24-hour or long-term mortality (RR = 1.13; 95% CI, 0.68 - 1.88; P = .63). Conversely, plasma transfusion alone exhibited a 28% reduction in 24-hour mortality with a RR of 0.72 (95% CI, 0.53 - 0.99; P = .04). In-hospital mortality and length of hospital stay were mostly unaffected by p-RBCs or p-RBCs plus plasma, except for a notable three-day reduction in length of hospital stay with p-RBCs alone (MD = -3.00; 95% CI, -5.01 to -0.99; P = .003). Hematological parameter analysis revealed nuanced effects, including a four-unit increase in RBC requirements with p-RBCs (MD = 3.95; 95% CI, 0.69 - 7.21; P = .02) and a substantial reduction in plasma requirements with plasma transfusion (MD = -0.73; 95% CI, -1.28 to -0.17; P = .01).
Conclusion:
This study revealed that plasma transfusion alone was associated with a substantial decrease in 24-hour mortality. Meanwhile, p-RBCs alone or combined with plasma did not significantly impact 24-hour or long-term mortality. In-hospital mortality and length of hospital stay were generally unaffected by p-RBCs or p-RBCs plus plasma, except for a substantial reduction in length of hospital stay with p-RBCs alone.
This systematic review and meta-analysis explore the correlation between foreign language instruction and mathematical skills in young adolescents, highlighting the significance of high school mathematical education and the adaptability of the adolescent brain. Focused on students starting second language programs between ages 8 and 13, following PRISMA guidelines, this review included 25 studies (1978–2020) with 785,552 participants. Using a random-effects model, the overall effect size revealed a statistically significant relationship between our variables, indicating a threefold higher likelihood of passing or achieving higher grades in mathematical tests for language-learning students. Moderating variables analyses identified socioeconomic status (SES) and intervention length as influencers of observed heterogeneity, with SES being the primary factor. Sensitivity analyses, including adding potentially missing studies and removing outliers, confirmed the robustness of the overall effect. Nonetheless, additional research is needed to enhance global diversity and comprehensively understand the interplay between language learning and cognitive function.