392,240 papers found

The purpose of meta-interpretive literature reviews is to combine the individual findings of different studies into a single, coherent analysis (here: meta-studies). The positions on how to handle that differ enormously. This is reflected in the variety of terms and definitions for synonym circumstances, e.g. "meta-ana…

Meta-analysis, the statistical combination of results from several studies to produce a single estimate of a treatment effect or size of an association, continues to attract controversy. We illustrate and discuss the promises and limitations of meta-analysis. Meta-analysis of clinical trials can prevent delays in the i…

SUMMARY: Heterogeneity and genome search meta-analysis (HEGESMA) is a comprehensive software for performing genome scan meta-analysis, a quantitative method to identify genetic regions (bins) with consistently increased linkage score across multiple genome scans, and for testing the heterogeneity of the results of each…

The overall goal of a systematic review or meta-analysis is to combine results of previous studies to arrive at summary conclusions about a body of research. In radiology, systematic reviews or meta-analyses can be used to calculate a summary estimate of effect size of a treatment that used imaging data to assess outco…

The overwhelming increase in the quantity of clinical evidence has led to detachment of the evidence and practice because new evidence can be integrated into clinical practice only after it has been critically appraised and synthesized on the basis of the existing evidence. Because many clinicians lack the skills and t…

Meta-analysis is becoming a key partner of Evidence-Based Medicine. This statistical tool is currently used in most systematic reviews and allows to quantitatively synthesize the results of several studies by a single weighted average whose precision is indicated by the confidence interval. A standardized graphical rep…

This tutorial on advanced statistical methods for meta-analysis can be seen as a sequel to the recent Tutorial in Biostatistics on meta-analysis by Normand, which focused on elementary methods. Within the framework of the general linear mixed model using approximate likelihood, we discuss methods to analyse univariate…

We study recently developed nonparametric methods for estimating the number of missing studies that might exist in a meta-analysis and the effect that these studies might have had on its outcome. These are simple rank-based data augmentation techniques, which formalize the use of funnel plots. We show that they provide…

Systematic reviews and meta-analyses are techniques of data retrieval and analysis which complement traditional narrative reviews. They are widely used in clinical medicine and are finding an increasing role in anatomical pathology. Performing high quality systematic review and meta-analysis requires the accumulation o…

Publication bias is a widespread problem that may seriously distort attempts to estimate the effect under investigation. The literature is reviewed to determine features of the design and execution of both single studies and meta-analyses leading to publication bias, and the role the author, journal editor, and reviewe…

Over the last 20 years, the development of meta-analysis has been aimed at obtaining objective synthesis of the available results on specific research questions. The main achievements of meta-analysis include the application of techniques to perform systematic literature searches and to obtain unbiased selection of stu…

This paper describes the use of a linear mixed-effects regression model as a framework for the meta-analysis of published data. It generalizes the random-effects models used by DerSimonian and Laird (1986, Controlled Clinical Trials 7, 177-188) and Begg and Pilote (1991, Biometrics 47, 899-906), and describes the use o…

Systematic error or bias in a review is hard to avoid when the review is executed without a clearly defined research question and methodology. The methods of systematic review and meta-analysis are designed to address these and other sources of bias with the goal of producing the most valid and precise estimate of effe…

A method is presented for estimating the treatment effect in a meta-analysis when some of the studies are comparative studies, and the rest are noncomparative, historical control studies. A random-effects model is used in which the baseline effect in each study is random, but the treatment effect is constant. With this…

The increasing availability of transcriptomic data has created new opportunities for integrating gene expression studies across biological systems and conditions. However, differences in experimental design, sequencing platforms, and sample composition introduce substantial heterogeneity, limiting direct comparability…

In prognosis studies with time-to-event outcomes, the survivals of groups with high/low biomarker expression are often estimated by the Kaplan-Meier method, and the difference between groups is measured by the hazard ratios (HRs). Since the high/low expressions are usually determined by study-specific cutoff values, sy…

The contrast-based model (CBM) is the most popular network meta-analysis (NMA) method, although alternative approaches, e.g., the baseline model (BM), have been proposed but seldom used. This article aims to illuminate the difference between the CBM and BM and explores when they produce different results. These models…

Model-based meta-analysis (MBMA) can be utilized to synthesize literature data and predict drug efficacy, particularly suitable for constructing external comparator arms for non-randomized controlled trials (NRCTs). This study evaluated the reliability of MBMA by comparing covariate models generated through MBMA to ind…

Meta-analyses represent a key milestone in evidence-based medicine, integrating data from multiple studies to generate more robust estimates of an intervention's effects. By pooling information from various trials or observational studies, meta-analyses enhance statistical power, elucidate subgroup effects, and guide h…

Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to "map" the outc…