382,077 papers found

OBJECTIVE: Dose-response meta-analysis (DRMA) is widely applied to investigate the dose-specific relationship between independent and dependent variables. Such methods have been in use for over 30 years and are increasingly employed in healthcare and clinical decision-making. In this article, we give an overview of the…

In conventional frequentist power analysis, one often uses an effect size estimate, treats it as if it were the true value, and ignores uncertainty in the effect size estimate for the analysis. The resulting sample sizes can vary dramatically depending on the chosen effect size value. To resolve the problem, we propose…

A widely used method in classic random-effects meta-analysis is the DerSimonian-Laird method. An alternative meta-analytical approach is the Hartung-Knapp method. This article reports results of an empirical comparison and a simulation study of these two methods and presents corresponding analytical results. For t…

Recently, multiple imputation has been proposed as a tool for individual patient data meta-analysis with sporadically missing observations, and it has been suggested that within-study imputation is usually preferable. However, such within study imputation cannot handle variables that are completely missing within studi…

Many meta-analyses report using 'Cochran's Q test' to assess heterogeneity of effect-size estimates from the individual studies. Some authors cite work by W. G. Cochran, without realizing that Cochran deliberately did not use Q itself to test for heterogeneity. Further, when heterogeneity is absent, the actual null dis…

For network meta-analysis (NMA), we usually assume that the treatment arms are independent within each included trial. This assumption is justified for parallel design trials and leads to a property we call consistency of variances for both multi-arm trials and NMA estimates. However, the assumption is violated for tri…

There is a growing interest of researchers in meta-analytic methods for comparing variances as a means to answer questions on between-group differences in variability. When measurements are fallible, however, the variance of an outcome reflects both the variance of the true scores and the error variance. Consequently,…

Recommendations for the analysis of competing risks in the context of randomized clinical trials are well established. Meta-analysis of individual patient data (IPD) is the gold standard for synthesizing evidence for clinical interpretation based on multiple studies. Surprisingly, no formal guidelines have been yet pro…

Network meta-analysis has evolved into a core method for evidence synthesis in health care. In network meta-analysis, 3 or more treatments for a given medical condition are compared, based on a number of clinical studies, usually randomized controlled trials. Often, many different endpoints are investigated, related to…

One of the reasons for the popularity of meta-analysis is the notion that these analyses will possess more power to detect effects than individual studies. This is inevitably the case under a fixed-effect model. However, the inclusion of the between-study variance in the random-effects model, and the need to estimate t…

Meta-analysis is increasingly used to synthesize major patterns in the large literatures within ecology and evolution. Meta-analytic methods that do not account for the process of observing data, which we may refer to as 'informal meta-analyses', may have undesirable properties. In some cases, informal meta-analyses ma…

Individual participant data (IPD) is the backbone of scientific inquiry and important to a meta-analysis for a variety of reasons. It is therefore important to be able to access IPD, and yet, obstacles persist that make it difficult for meta-analysts, as well as interested primary study analysts, to obtain it. In this…

Recent work has made important contributions to the development of causally-interpretable meta-analysis. These methods transport treatment effects estimated in a collection of randomized trials to a target population of interest. Ideally, estimates targeted toward a specific population are more interpretable and releva…

Meta-analyses often include multiple dependent effect sizes, yet current methods typically neglect the resulting within-study dependencies or fail to address model uncertainty and publication bias adequately. We extend robust Bayesian meta-analysis (RoBMA) to a multilevel framework, simultaneously handling within-study…

The performance of statistical methods is frequently evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, available data-generating models (DGMs) are restricted to either inclusion of two-armed trials or the fixed-effect model. Based on data-generation in the pairwise case…

The random effect approach for meta-analysis was motivated by a lack of consistent assessment of homogeneity of treatment effect before pooling. The random effect model assumes that the distribution of the treatment effect is fully heterogenous across the experiments. However, other models arising by grouping some of t…

In meta-analysis, the random-effects model is often used to account for heterogeneity. The model assumes that heterogeneity has an additive effect on the variance of effect sizes. An alternative model, which assumes multiplicative heterogeneity, has been little used in the medical statistics community, but is widely us…

In real life and somewhat contrary to biostatistical textbook knowledge, sensitivity and specificity (and not only predictive values) of diagnostic tests can vary with the underlying prevalence of disease. In meta-analysis of diagnostic studies, accounting for this fact naturally leads to a trivariate expansion of the…

In the past decade, a new statistical method-network meta-analysis-has been developed to address limitations in traditional pairwise meta-analysis. Network meta-analysis incorporates all available evidence into a general statistical framework for comparisons of multiple treatments. Bayesian network meta-analysis, as pr…

Two methods of quantifying heterogeneity between studies in meta-analysis were studied. One method quantified the proportion of the total variance of the effect estimate due to variation between studies (RI), and the other calibrated the variance between studies to the size of the effect itself through a between-study…