392,095 papers found

Diagnostic test accuracy studies observe the result of a gold standard procedure that defines the presence or absence of a disease and the result of a diagnostic test. They typically report the number of true positives, false positives, true negatives and false negatives. However, diagnostic test outcomes can also be e…

OBJECTIVES: Network meta-analysis (NMA) may produce more precise estimates of treatment effects than pairwise meta-analysis. We examined the relative contribution of network paths of different lengths to estimates of treatment effects. STUDY DESIGN AND SETTING: We analyzed 213 published NMAs. We categorized network sh…

Diagnostic accuracy studies often evaluate diagnostic tests at several threshold values, aiming to make recommendations on optimal thresholds for use in practice. Methods for meta-analysis of full receiver operating characteristic (ROC) curves have been proposed but still have deficiencies. We recently proposed a param…

Funnel plots have been widely used to detect small-study effects in the results of univariate meta-analyses. However, there is no existing visualization tool that is the counterpart of the funnel plot in the multivariate setting. We propose a new visualization method, the galaxy plot, which can simultaneously present t…

OBJECTIVES: The inconsistency demonstrated across strata when using different scales has been attributed to quality scores, and stratification continues to be done using risk of bias domain judgments. This study examines if restricting primary meta-analyses to studies at low risk of bias or presenting meta-analyses str…

This paper studies the performance of the FAT-PET-PEESE (FPP) procedure, a commonly employed approach for addressing publication bias in the economics and business meta-analysis literature. The FPP procedure is generally used for 3 purposes: (1) to test whether a sample of estimates suffers from publication bias, (2) t…

Meta-analysis can average estimates of multiple parameters, such as a treatment's effect on multiple outcomes, across studies. Univariate meta-analysis (UVMA) considers each parameter individually, while multivariate meta-analysis (MVMA) considers the parameters jointly and accounts for the correlation between their es…

Meta-analysis of diagnostic studies is still a rapidly developing area of biostatistical research. Especially, there is an increasing interest in methods to compare different diagnostic tests to a common gold standard. Restricting to the case of two diagnostic tests, in these meta-analyses the parameters of interest ar…

Systematic reviews provide a method for collating and synthesizing research, and are used to inform healthcare decision making by clinicians, consumers and policy makers. A core component of many systematic reviews is a meta-analysis, which is a statistical synthesis of results across studies. In this review article, w…

Evidence-Based Anatomy (EBA) is the concept of applying evidence-based principles and research methods to the anatomical sciences. While narrative reviews are common in the anatomical sciences, true systematic reviews (SR) and meta-analysis (MA) are only beginning to grow in popularity. In order to enhance the quality…

Whilst it is common in clinical trials to use the results of tests at one phase to decide whether to continue to the next phase and to subsequently design the next phase, we show that this can lead to biased results in evidence synthesis. Two new kinds of bias associated with accumulating evidence, termed 'sequential d…

In psychological studies, multivariate outcomes measured on the same individuals are often encountered. Effects originating from these outcomes are consequently dependent. Multivariate meta-analysis examines the relationships of multivariate outcomes by estimating the mean effects and their variance-covariance matrices…

Bivariate random-effects models are currently widely used to synthesize pairs of test sensitivity and specificity across studies. Inferences drawn based on these models may be distorted in the presence of outlying or influential studies. Currently, subjective methods such as inspection of forest plots are used to ident…

The objective of this study was to describe general approaches for intervention meta-analysis available for quantitative data synthesis using the R software. We conducted an intervention meta-analysis using two types of data, continuous and binary, characterized by mean difference and odds ratio, respectively. The pack…

Meta-analysis statistically assesses the results (e.g., effect sizes) across independent studies that are conducted in accordance with similar protocols and objectives. Current genomic meta-analysis studies do not perform extensive re-analysis on raw data because full data access would not be commonplace…

Meta-analyses are often used to synthesize the findings of studies examining the correlational relationship between two continuous variables. When only dichotomous measurements are available for one of the two variables, the biserial correlation coefficient can be used to estimate the product-moment correlation between…

Combining effect sizes from individual studies using random-effects meta-analysis models are commonly applied in high-dimensional gene expression data. However, unknown study heterogeneity can arise from inconsistencies in sample quality and experimental conditions. High heterogeneity of effect sizes can reduce statist…

OBJECTIVE: This paper demonstrates how to conduct a meta-analysis that includes both between-group and single-case design (SCD) studies. The example studies whether choice-making interventions decrease challenging behaviors performed by people with disabilities. METHODS: We used a between-case d-statistic to conduct a…

Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which w…