Kurex Sidik

TL;DR: This paper discusses an alternative simple approach for constructing the confidence interval, based on the t-distribution, which has improved coverage probability and is easy to calculate, and unlike some methods suggested in the statistical literature, no iterative computation is required.

TL;DR: In this paper, a simple method of estimating the heterogeneity variance in a random-effects model for meta-analysis is proposed, which is simple and easy to calculate and has improved bias compared with the most common estimator used in random effects meta analysis.

TL;DR: It is argued that inference about an overall effect should be based on the robust variance estimator or the weighted sample variance, which provide protection against the practice of using estimated weights in meta-analytical inference.

TL;DR: It is found that, despite the seeming suitability of the robust estimator for random effects meta-regression, the improved variance estimator of Knapp and Hartung (2003) yields the best performance among the three estimators, and thus may provide the best protection against errors in the estimated weights.

TL;DR: In this article, the authors compare the empirical coverage probability of confidence intervals based on both the standard normal distribution and the t-distribution, in conjunction with several methods of estimating the heterogeneity variance for a standardized mean difference.