392,269 papers found

Sample size and statistical power are important factors to consider when planning a research synthesis. Power analysis methods have been developed for fixed effect or random effects models, but until recently these methods were limited to simple data structures with a single, independent effect per study. Recent work h…

There is an extensive literature on methods for meta-analysis of diagnostic test accuracy, but it mainly focuses on a single test. A multinomial generalised linear mixed model was recently proposed for the joint meta-analysis of studies comparing two tests on the same participants in a paired tests design with a gold s…

The systematic review and meta-analysis titled "The Effects of Dabrafenib and/or Trametinib Treatment in BRAF V600-Mutant Glioma" provides a critical evaluation of these targeted therapies for a challenging subset of gliomas. This review is notable for its comprehensive data integration, offering a robust assessment of…

By manipulating inclusion criteria, one can prove whatever point one wishes in meta-analysis. This critique examines a recent meta-analysis claiming lithium ineffectiveness for suicidality, based on three biased features: inclusion of many large studies specifically designed to exclude suicidality, producing zero suici…

The collection of recommendations and guidelines for conducting and reporting neuroimaging meta-analyses, called "Ten simple rules for neuroimaging meta-analysis" by Müller et al., has been published for a few years. Here, the papers citing this reference were examined to evaluate the rationale of the quotations a…

BACKGROUND: Some meta-analyses have proved the superiority of non-pharmacological interventions in overactive bladder (OAB), but the best choice is still controversial. AIM: To assess the most effective interventions in female with OAB. METHODS: Studies for relevant randomized controlled trials which compare differen…

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox f…

Public health researchers may have to decide whether to perform a meta-analysis including only high-quality randomized clinical trials (RCTs) or whether to include a mixture of all the available evidence, namely RCTs of varying quality and observational studies (OS). The main hurdle when combining disparate evidence in…

Network meta-analysis (NMA) is a statistical technique for the comparison of treatment options. Outcomes of Bayesian NMA include estimates of treatment effects, and the probabilities that each treatment is ranked best, second best and so on. How exactly network topology affects the accuracy and precision of these outco…

The effect sizes of studies included in a meta-analysis do often not share a common true effect size due to differences in for instance the design of the studies. Estimates of this so-called between-study variance are usually imprecise. Hence, reporting a confidence interval together with a point estimate of the amount…

The synthesis of standardized regression coefficients is still a controversial issue in the field of meta-analysis. The difficulty lies in the fact that the standardized regression coefficients belonging to regression models that include different sets of covariates do not represent the same parameter, and thus their d…

In medical sciences, a disease condition is typically associated with multiple risk and protective factors. Although many studies report results of multiple factors, nearly all meta-analyses separately synthesize the association between each factor and the disease condition of interest. The collected studies usually re…

Publication bias is a serious problem in systematic reviews and meta-analyses, which can affect the validity and generalization of conclusions. Currently, approaches to dealing with publication bias can be distinguished into two classes: selection models and funnel-plot-based methods. Selection models use weight functi…

Bayesian meta-analysis has been more frequently utilized for synthesizing safety and efficacy information to support landmark decision-making due to its flexibility of incorporating prior information and availability of computing software. However, when the outcome is binary and the events are rare, where event counts…

In a bivariate meta-analysis, the number of diagnostic studies involved is often very low so that frequentist methods may result in problems. Using Bayesian inference is particularly attractive as informative priors that add a small amount of information can stabilise the analysis without overwhelming the data. However…

BACKGROUND: Network meta-analysis for multiple treatment comparisons has been a major development in evidence synthesis methodology. The validity of a network meta-analysis, however, can be threatened by inconsistency in evidence within the network. One particular issue of inconsistency is how to directly evaluate the…

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…

Phase I trials aim to establish appropriate clinical and statistical parameters to guide future clinical trials. With individual trials typically underpowered, systematic reviews and meta-analysis are desired to assess the totality of evidence. A high percentage of zero or missing outcomes often complicate such efforts…

We develop a Bayesian multinomial network meta-analysis model for unordered (nominal) categorical outcomes that allows for partially observed data in which exact event counts may not be known for each category. This model properly accounts for correlations of counts in mutually exclusive categories and enables proper c…

This article examines the performance of the updated quality effects (QE) estimator for meta-analysis of heterogeneous studies. It is shown that this approach leads to a decreased mean squared error (MSE) of the estimator while maintaining the nominal level of coverage probability of the confidence interval. Extensive…