A Comparison of Methods to Detect Publication Bias for Meta-analysis of Continuous Data

Idris, Nik Ruzni Nik

doi:10.3923/jas.2012.1413.1417

Cited by 30 publications

(15 citation statements)

References 18 publications

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“…Given widespread concern that both funnel-plot asymmetry and trim-and-fill undercorrect for publication bias (Carter et al, 2019;Idris, 2012;Simonson, Nelson, & Simons, 2014).…”

Section: Quantitative Corrections For Publication Biasmentioning

confidence: 99%

Assessing and adjusting for publication bias in the relationship between anxiety and the error-related negativity

Saunders

Inzlicht

2020

International Journal of Psychophysiology

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Many clinical neuroscience investigations have suggested that trait anxiety is associated with increased neural reactivity to mistakes in the form of an event-related potential called the error-related negativity (ERN). Several recent meta-analyses indicated that the anxiety-ERN association was of a small-to-medium effect size, however, these prior investigations did not comprehensively adjust effect sizes for publication bias. Here, in an updated meta-analysis (k=58, N=3819), we found support for an uncorrected effect size of r =-.19, and applied a range of methods to test for and correct publication bias (trim-and-fill, PET, PEESE, Peters' test, threeparameter selection model). The majority of bias-correction methods suggested that the correlation between anxiety and the ERN is non-zero, but smaller than the uncorrected effect size (average adjusted effect size: r =-.12, range: r =-.05 to-.18). Moderation analyses also revealed more robust effects for clinical anxiety and anxious samples characterised by worry, however, it should be noted that these larger effects were also associated with elevated indicators of publication bias relative to the overall analysis. Mixed anxiety and sub-clinical anxiety were not associated with the amplitude of the ERN. Our results suggest that the anxiety-ERN relationship survives multiple corrections for publication bias, albeit not among all sub-types and populations of anxiety. Nevertheless, only 50% of the studies included in our analysis reported significant results, indicating that future research exploring the anxiety-ERN relationship would benefit from increased statistical power.

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“…Given widespread concern that both funnel-plot asymmetry and trim-and-fill undercorrect for publication bias (Carter et al, 2019;Idris, 2012;Simonson, Nelson, & Simons, 2014).…”

Section: Quantitative Corrections For Publication Biasmentioning

confidence: 99%

Assessing and adjusting for publication bias in the relationship between anxiety and the error-related negativity

Saunders

Inzlicht

2020

International Journal of Psychophysiology

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“…Publication bias was evaluated through funnel-plot asymmetry, Begg and Mazumdar rank correlation, Egger's regression, Fail-safe N test, and Trim and fill method. If conflictual results of publication bias arose from these analyses, the Trim and fill method was designated (Idris, 2012) with discussion of both investigators.…”

Section: Data Synthesis and Statistical Analysismentioning

confidence: 99%

Lamotrigine augmentation in treatment-resistant unipolar depression: A comprehensive meta-analysis of efficacy and safety

et al. 2019

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Objective: Augmentation strategies are commonly applied when an individual is unresponsive to antidepressant monotherapy. Lamotrigine is currently considered at best only as second line augmentation for treatment-resistant unipolar depression while its clinical efficacy and safety profiles remain inconclusive. We intended to assess the therapeutic effects and safety profiles of lamotrigine augmentation in patients with treatment-resistant unipolar depression by conducting a meta-analysis. Methods: MEDLINE, Embase, EBSCO, Cochrane, Web of Science, Scopus, Wanfang and Ariniti databases were searched. Coprimary outcomes, including changes in severity of depression and response rate, were measured in this study. Secondary outcomes were defined as the safety profile of the intervention, including reported discontinuation rate and adverse events. Results: Eight double-blinded randomized controlled trials with 677 patients overall were included. Significant improvements in Hamilton Rating Scale for Depression scores and response rates were shown in lamotrigine augmentation groups compared with control groups, of which the pooled result of six Chinese studies showed positive effects of Hamilton Rating Scale for Depression improvement while the pooled result of two non-Chinese studies was statistically non-significant. Patients with more severe illness and longer duration of illness were more effectively treated with lamotrigine augmentation. The magnitude of depression improvement after lamotrigine augmentation was higher in patients treated with selective serotonin reuptake inhibitors than those treated with serotonin-norepinephrine reuptake inhibitors. Lamotrigine augmentation is well-tolerated in terms of all-cause discontinuation rate and adverse events. Conclusions: Lamotrigine augmentation may serve as a possible choice for patients with treatment-resistant unipolar depression and further trials are warranted to clarify the optimal dosage of lamotrigine augmentation together with the treatment duration and safety over time.

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“…This also accounts for multilevel meta-analytic research. In scientific literature, there is a considerable and ongoing debate on the appropriateness of the available methods and each method seems to have its own limitations (see, for instance, Egger, DaveySmith, & Altman, 2001;Nakagawa & Santos, 2012;Nik Idris, 2012;Peters, Sutton, Jones, Abrams, & Rushton, 2007;Terrin, Schmid, Lau, & Olkin, 2003). Therefore, selecting the most appropriate method for dealing with missing data may not be straightforward.…”

Section: Missing Data and Size Of The Data Setmentioning

confidence: 99%

Fitting three-level meta-analytic models in R: A step-by-step tutorial

Assink¹,

Wibbelink²

2016

TQMP

795

846

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Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of effect sizes. However, this method is relatively unknown among researchers and, to date, has not been widely used in meta-analytic research. Therefore, the purpose of this tutorial was to show how a three-level random effects model can be applied to meta-analytic models in R using the rma.mv function of the metafor package. This application is illustrated by taking the reader through a step-by-step guide to the multilevel analyses comprising the steps of (1) organizing a data file; (2) setting up the R environment; (3) calculating an overall effect; (4) examining heterogeneity of within-study variance and between-study variance; (5) performing categorical and continuous moderator analyses; and (6) examining a multiple moderator model. By example, the authors demonstrate how the multilevel approach can be applied to meta-analytically examining the association between mental health disorders of juveniles and juvenile offender recidivism. In our opinion, the rma.mv function of the metafor package provides an easy and flexible way of applying a multi-level structure to meta-analytic models in R. Further, the multilevel meta-analytic models can be easily extended so that the potential moderating influence of variables can be examined.

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A Comparison of Methods to Detect Publication Bias for Meta-analysis of Continuous Data

Cited by 30 publications

References 18 publications

Assessing and adjusting for publication bias in the relationship between anxiety and the error-related negativity

Assessing and adjusting for publication bias in the relationship between anxiety and the error-related negativity

Lamotrigine augmentation in treatment-resistant unipolar depression: A comprehensive meta-analysis of efficacy and safety

Fitting three-level meta-analytic models in R: A step-by-step tutorial

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