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- Forkman, Johannes
(författare)
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A REML method for the evidence-splitting model in network meta-analysis
- 2024
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Ingår i: Research Synthesis Methods. - 1759-2879 .- 1759-2887. ; 15, s. 198-212
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Tidskriftsartikel (refereegranskat)abstract
- Checking for possible inconsistency between direct and indirect evidence is an important task in network meta-analysis. Recently, an evidence-splitting (ES) model has been proposed, that allows separating direct and indirect evidence in a network and hence assessing inconsistency. A salient feature of this model is that the variance for heterogeneity appears in both the mean and the variance structure. Thus, full maximum likelihood (ML) has been proposed for estimating the parameters of this model. Maximum likelihood is known to yield biased variance component estimates in linear mixed models, and this problem is expected to also affect the ES model. The purpose of the present paper, therefore, is to propose a method based on residual (or restricted) maximum likelihood (REML). Our simulation shows that this new method is quite competitive to methods based on full ML in terms of bias and mean squared error. In addition, some limitations of the ES model are discussed. While this model splits direct and indirect evidence, it is not a plausible model for the cause of inconsistency.
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- Kirvalidze, Mariam, et al.
(författare)
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Estimating pairwise overlap in umbrella reviews : Considerations for using the corrected covered area (CCA) index methodology
- 2023
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Ingår i: Research Synthesis Methods. - : John Wiley & Sons. - 1759-2879 .- 1759-2887. ; 14:5, s. 764-767
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Forskningsöversikt (refereegranskat)abstract
- Umbrella reviews (reviews of systematic reviews) are increasingly used to synthesize findings from systematic reviews. One important challenge when pooling data from several systematic reviews is publication overlap, that is, the same primary publications being included in multiple reviews. Pieper et al. have proposed using the corrected covered area (CCA) index to quantify the degree of overlap between systematic reviews to be pooled in an umbrella review. Recently, this methodology has been integrated in Excel- or R-based tools for easier use. In this short letter, we highlight an important consideration for using the CCA methodology for pairwise overlap assessment, especially when reviews include varying numbers of primary publications, and we urge researchers to fine-tune this method and exercise caution when review exclusion decisions are based on its output.
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- Moulin, Thiago, et al.
(författare)
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Using collaboration networks to identify authorship dependence in meta-analysis results
- 2020
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Ingår i: Research Synthesis Methods. - : WILEY. - 1759-2879 .- 1759-2887. ; 11:5, s. 655-668
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Tidskriftsartikel (refereegranskat)abstract
- Meta-analytic methods are powerful resources to summarize the existing evidence concerning a given research question and are widely used in many academic fields. Meta-analyzes can also be used to study sources of heterogeneity and bias among results, which should be considered to avoid inaccuracies. Many of these sources can be related to study authorship, as both methodological heterogeneity and researcher bias may lead to deviations in results between different research groups. In this work, we describe a method to objectively attribute study authorship within a given meta-analysis to different research groups by using graph cluster analysis of collaboration networks. We then provide empirical examples of how the research group of origin can impact effect size in distinct types of meta-analyzes, demonstrating how non-independence between within-group results can bias effect size estimates if uncorrected. Finally, we show that multilevel random-effects models using research group as a level of analysis can be a simple tool for correcting for authorship dependence in results.
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- Poom, Leo, et al.
(författare)
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Accuracy of conversion formula for effect sizes : A Monte Carlo simulation
- 2022
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Ingår i: Research Synthesis Methods. - : John Wiley & Sons. - 1759-2879 .- 1759-2887. ; 13:4, s. 508-519
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Tidskriftsartikel (refereegranskat)abstract
- In meta-analysis, effect sizes often need to be converted into a common metric. For this purpose conversion formulas have been constructed; some are exact, others are approximations whose accuracy has not yet been systematically tested. We performed Monte Carlo simulations where samples with pre-specified population correlations between the x and y-variables were drawn from a normally distributed population. A number of commonly used effect size measures and statistics were calculated from each sample. Using several available conversion formula these statistics were converted into Pearson r and Cohen's d and compared to r and d calculated directly from the original data. Converted values were systematically lower than the directly calculated values. While conversions to d were quite accurate, some of the conversions to r resulted in large biases. These systematic errors can in most cases be adjusted for by simply multiplying the converted values with a corresponding correction factor.
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