392,240 papers found

Meta-analysis is a popular statistical technique in biomedical research. In particular, meta-analysis can assist clinicians in determining whether an intervention is effective or which intervention is most effective. Conventional meta-analysis combines aggregated information from multiple published studies and addresse…

Random effects meta-analysis model is an important tool for integrating results from multiple independent studies. However, the standard model is based on the assumption of normal distributions for both random effects and within-study errors, making it susceptible to outlying studies. Although robust modeling using the…

BACKGROUND: Network meta-analysis (NMA) is a widely used method for synthesizing evidence from multiple interventions for a medical condition. However, NMA applications typically ignore the crucial role of drug dosage on intervention effects. Traditional NMAs either consider each intervention dose as an independent nod…

Meta-analysis is a statistical method for combining quantitative results across studies. A fundamental decision in undertaking a meta-analysis is choosing an appropriate model for analysis. This is the second of two companion articles which have the joint aim of describing the different meta-analysis models. In the fir…

OBJECTIVES: Network meta-analysis (NMA) is becoming a popular statistical tool for analyzing a network of evidence comparing more than two interventions. A particular advantage of NMA over pairwise meta-analysis is its ability to simultaneously compare multiple interventions including comparisons not previously trialed…

Network meta-analysis is a popular method to synthesize the information obtained in a systematic review of studies (e.g., randomized clinical trials) involving subsets of multiple treatments of interest. The dominant method of analysis employs within-study information on treatment contrasts and integrates this over a n…

Our recently published systematic review and meta-analysis of heart failure (HF) remote monitoring using implantable devices (Hajduczok et al. in HF Reviews 1-20, 1) has been updated to reflected new data from the GUIDE-HF trial (Lindenfeld et al. in Lancet 398(10304):991-1001, 2). Data from randomi…

OBJECTIVE: In evidence synthesis practice, researchers often face the problem of how to deal with zero-events. Inappropriately dealing with zero-events studies may lead to research waste and mislead healthcare practice. We propose a framework to guide researchers to better deal with zero-events in meta-analysis. STUDY…

Network meta-analysis (NMA) is a popular tool to synthesize direct and indirect evidence for simultaneously comparing multiple treatments, while evidence inconsistency greatly threatens its validity. One may use the inconsistency degrees of freedom (ICDF) to assess the potential that an NMA might suffer from inconsiste…

BACKGROUND: In pairwise meta-analysis, the contribution of each study to the pooled estimate is given by its weight, which is based on the inverse variance of the estimate from that study. For network meta-analysis (NMA), the contribution of direct (and indirect) evidence is easily obtained from the diagonal elements o…

Meta-analysis of 2 × 2 tables is common and useful in research topics including analysis of adverse events and survey research data. Fixed-effects inference typically centers on measures of association such as the Cochran-Mantel-Haenszel statistic or Woolf's estimator, but relies on assuming exact homogenei…

RATIONALE, AIMS, AND OBJECTIVES: Heterogeneity is a critical issue in meta-analysis, because it implies the appropriateness of combining the collected studies and impacts the reliability of the synthesized results. The Q test is a traditional method to assess heterogeneity; however, because it does not have an intuitiv…

A bivariate generalised linear mixed model is often used for meta-analysis of test accuracy studies. The model is complex and requires five parameters to be estimated. As there is no closed form for the likelihood function for the model, maximum likelihood estimates for the parameters have to be obtained numerically. A…

Assessing the magnitude of heterogeneity in a meta-analysis is important for determining the appropriateness of combining results. The most popular measure of heterogeneity, I(2) , was derived under an assumption of homogeneity of the within-study variances, which is almost never true, and the alternative estimator, R^…

We consider a study-level meta-analysis with a normally distributed outcome variable and possibly unequal study-level variances, where the object of inference is the difference in means between a treatment and control group. A common complication in such an analysis is missing sample variances for some studies. A frequ…