A simple confidence interval for meta‐analysis

  title={A simple confidence interval for meta‐analysis},
  author={Kurex Sidik and Jeffrey N. Jonkman},
  journal={Statistics in Medicine},
In the context of a random effects model for meta‐analysis, a number of methods are available to estimate confidence limits for the overall mean effect. A simple and commonly used method is the DerSimonian and Laird approach. This paper discusses an alternative simple approach for constructing the confidence interval, based on the t‐distribution. This approach has improved coverage probability compared to the DerSimonian and Laird method. Moreover, it is easy to calculate, and unlike some… 

A simple method for inference on an overall effect in meta-analysis.

A new approach is proposed, which is simple to use, and has coverage probabilities better than the alternatives, based on extensive simulation, and is called the ‘quantile approximation’ method.

Methods to calculate uncertainty in the estimated overall effect size from a random‐effects meta‐analysis

This paper aims to provide a comprehensive overview of available methods for calculating point estimates, confidence intervals, and prediction intervals for the overall effect size under the random‐effects model, and indicates whether some methods are preferable than others by considering the results of comparative simulation and real‐life data studies.

Confidence intervals for the amount of heterogeneity in meta‐analysis

A novel method for constructing confidence intervals for the amount of heterogeneity in the effect sizes is proposed that guarantees nominal coverage probabilities even in small samples when model assumptions are satisfied and yields the most accurate coverage probabilities under conditions more analogous to practice.

A refined method for multivariate meta-analysis and meta-regression

It is found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference than the more conventional approach.

Robust confidence intervals for trend estimation in meta-analysis with publication bias

Confidence interval (CI) is very useful for trend estimation in meta-analysis. It provides a type of interval estimate of the regression slope as well as an indicator of the reliability of the

Random effects meta‐analysis: Coverage performance of 95% confidence and prediction intervals following REML estimation

Researchers should be cautious in deriving 95% prediction intervals following a frequentist random‐effects meta‐analysis until a more reliable solution is identified, especially when there are few studies.

Simple heterogeneity variance estimation for meta‐analysis

Summary.  A simple method of estimating the heterogeneity variance in a random‐effects model for meta‐analysis is proposed. The estimator that is presented is simple and easy to calculate and has

Meta-Analysis of a Very Low Proportion Through Adjusted Wald Confidence Intervals

  • Afreixo
  • Psychology
    Open Access Biostatistics & Bioinformatics
  • 2019
Meta-analysis, which is a statistical technique for combining the findings from independent studies, can be used in many fields of research, having high importance in clinical and epidemiological

A confidence interval robust to publication bias for random‐effects meta‐analysis of few studies

A variation of the method by Henmi and Copas employing an improved estimator of the between-study heterogeneity, in particular when dealing with few studies only is proposed, and it is found that the method outperforms the others in terms of coverage probabilities.

Confidence intervals for random effects meta‐analysis and robustness to publication bias

A new confidence interval is proposed that has better coverage than the DerSimonian-Laird method, and that is less sensitive to publication bias, and is centred on a fixed effects estimate, but allow for heterogeneity by including an assessment of the extra uncertainty induced by the random effects setting.



A comparison of statistical methods for meta-analysis.

It is shown that the commonly used DerSimonian and Laird method does not adequately reflect the error associated with parameter estimation, especially when the number of studies is small, and three methods currently used for estimation within the framework of a random effects model are considered.

A likelihood approach to meta-analysis with random effects.

It is concluded that likelihood based methods are preferred to the standard method in undertaking random effects meta-analysis when the value of sigma B2 has an important effect on the overall estimated treatment effect.

Meta-analysis: formulating, evaluating, combining, and reporting.

This article presents a tutorial on meta-analysis intended for anyone with a mathematical statistics background, focused on analytic methods for estimation of the parameters of interest.

Heterogeneity and statistical significance in meta-analysis: an empirical study of 125 meta-analyses.

This work studied 125 meta-analyses representative of those performed by clinical investigators to examine empirically how assessment of treatment effect and heterogeneity may differ when different methods are utilized, and presents two exceptions to these observations.

Meta-analysis in clinical trials.

Statistical Methods for Meta-Analysis

Preface. Introduction. Data Sets. Tests of Statistical Significance of Combined Results. Vote-Counting Methods. Estimation of a Single Effect Size: Parametric and Nonparametric Methods. Parametric

Theory and Application of the Linear Model

This book integrates the linear statistical model within the context of analysis of variance, correlation and regression, and design of experiments and is a time tested, authoritative resource for experimenters, statistical consultants, and students.