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Statistical properties of methods based on the Q-statistic for constructing a confidence interval for the between-study variance in meta-analysis.

ABSTRACT: The effect sizes of studies included in a meta-analysis do often not share a common true effect size due to differences in for instance the design of the studies. Estimates of this so-called between-study variance are usually imprecise. Hence, reporting a confidence interval together with a point estimate of the amount of between-study variance facilitates interpretation of the meta-analytic results. Two methods that are recommended to be used for creating such a confidence interval are the Q-profile and generalized Q-statistic method that both make use of the Q-statistic. These methods are exact if the assumptions underlying the random-effects model hold, but these assumptions are usually violated in practice such that confidence intervals of the methods are approximate rather than exact confidence intervals. We illustrate by means of two Monte-Carlo simulation studies with odds ratio as effect size measure that coverage probabilities of both methods can be substantially below the nominal coverage rate in situations that are representative for meta-analyses in practice. We also show that these too low coverage probabilities are caused by violations of the assumptions of the random-effects model (ie, normal sampling distributions of the effect size measure and known sampling variances) and are especially prevalent if the sample sizes in the primary studies are small.

SUBMITTER: van Aert RCM

PROVIDER: S-EPMC6590162 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Statistical properties of methods based on the Q-statistic for constructing a confidence interval for the between-study variance in meta-analysis.

van Aert Robbie C M RCM van Assen Marcel A L M MALM Viechtbauer Wolfgang W

Research synthesis methods 20190128 2

The effect sizes of studies included in a meta-analysis do often not share a common true effect size due to differences in for instance the design of the studies. Estimates of this so-called between-study variance are usually imprecise. Hence, reporting a confidence interval together with a point estimate of the amount of between-study variance facilitates interpretation of the meta-analytic results. Two methods that are recommended to be used for creating such a confidence interval are the Q-pr ...[more]

PMID: 30589219

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Project description:BackgroundThe intraclass correlation coefficient (ICC) is widely used in biomedical research to assess the reproducibility of measurements between raters, labs, technicians, or devices. For example, in an inter-rater reliability study, a high ICC value means that noise variability (between-raters and within-raters) is small relative to variability from patient to patient. A confidence interval or Bayesian credible interval for the ICC is a commonly reported summary. Such intervals can be constructed employing either frequentist or Bayesian methodologies.MethodsThis study examines the performance of three different methods for constructing an interval in a two-way, crossed, random effects model without interaction: the Generalized Confidence Interval method (GCI), the Modified Large Sample method (MLS), and a Bayesian method based on a noninformative prior distribution (NIB). Guidance is provided on interval construction method selection based on study design, sample size, and normality of the data. We compare the coverage probabilities and widths of the different interval methods.ResultsWe show that, for the two-way, crossed, random effects model without interaction, care is needed in interval method selection because the interval estimates do not always have properties that the user expects. While different methods generally perform well when there are a large number of levels of each factor, large differences between the methods emerge when the number of one or more factors is limited. In addition, all methods are shown to lack robustness to certain hard-to-detect violations of normality when the sample size is limited.ConclusionsDecision rules and software programs for interval construction are provided for practical implementation in the two-way, crossed, random effects model without interaction. All interval methods perform similarly when the data are normal and there are sufficient numbers of levels of each factor. The MLS and GCI methods outperform the NIB when one of the factors has a limited number of levels and the data are normally distributed or nearly normally distributed. None of the methods work well if the number of levels of a factor are limited and data are markedly non-normal. The software programs are implemented in the popular R language.

Dataset Information

Statistical properties of methods based on the Q-statistic for constructing a confidence interval for the between-study variance in meta-analysis.

Publications

Statistical properties of methods based on the Q-statistic for constructing a confidence interval for the between-study variance in meta-analysis.

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