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Alex J Sutton - One of the best experts on this subject based on the ideXlab platform.
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a fully bayesian application of the copas selection model for Publication Bias extended to network meta analysis
Statistics in Medicine, 2013Co-Authors: Dimitris Mavridis, Alex J Sutton, Andrea Cipriani, Georgia SalantiAbstract:The Copas parametric model is aimed at exploring the potential impact of Publication Bias via sensitivity analysis, by making assumptions regarding the probability of Publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta-analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for Publication Bias and to extend it to the case of network meta-analysis where each treatment is compared either with placebo or with a reference treatment creating a star-shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion.
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assessing Publication Bias in meta analyses in the presence of between study heterogeneity
Journal of The Royal Statistical Society Series A-statistics in Society, 2010Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Lesley Rushton, Alex J Sutton, Santiago G MorenoAbstract:Summary. Between-study heterogeneity and Publication Bias are common features of a meta-analysis that can be present simultaneously. When both are suspected, consideration must be made of each in the assessment of the other. We consider extended funnel plot tests for detecting Publication Bias, and selection modelling and trim-and-fill methods to adjust for Publication Bias in the presence of between-study heterogeneity. These methods are applied to two example data sets. Results indicate that ignoring between-study heterogeneity when assessing Publication Bias can be misleading, but that methods to test or adjust for Publication Bias in the presence of heterogeneity may not be powerful when the meta-analysis is not large. It is therefore unrealistic to expect to disentangle the effects of Publication Bias and heterogeneity reliably in all except the largest meta-analyses.
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assessment of regression based methods to adjust for Publication Bias through a comprehensive simulation study
BMC Medical Research Methodology, 2009Co-Authors: Santiago G Moreno, Jaime Peters, Keith R Abrams, T D Stanley, Alex J Sutton, A E Ades, Nicola J CooperAbstract:In meta-analysis, the presence of funnel plot asymmetry is attributed to Publication or other small-study effects, which causes larger effects to be observed in the smaller studies. This issue potentially mean inappropriate conclusions are drawn from a meta-analysis. If meta-analysis is to be used to inform decision-making, a reliable way to adjust pooled estimates for potential funnel plot asymmetry is required. A comprehensive simulation study is presented to assess the performance of different adjustment methods including the novel application of several regression-based methods (which are commonly applied to detect Publication Bias rather than adjust for it) and the popular Trim & Fill algorithm. Meta-analyses with binary outcomes, analysed on the log odds ratio scale, were simulated by considering scenarios with and without i) Publication Bias and; ii) heterogeneity. Publication Bias was induced through two underlying mechanisms assuming the probability of Publication depends on i) the study effect size; or ii) the p-value. The performance of all methods tended to worsen as unexplained heterogeneity increased and the number of studies in the meta-analysis decreased. Applying the methods conditional on an initial test for the presence of funnel plot asymmetry generally provided poorer performance than the unconditional use of the adjustment method. Several of the regression based methods consistently outperformed the Trim & Fill estimators. Regression-based adjustments for Publication Bias and other small study effects are easy to conduct and outperformed more established methods over a wide range of simulation scenarios.
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contour enhanced meta analysis funnel plots help distinguish Publication Bias from other causes of asymmetry
Journal of Clinical Epidemiology, 2008Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Alex J Sutton, Lesley RushtonAbstract:Abstract Objectives To present the contour-enhanced funnel plot as an aid to differentiating asymmetry due to Publication Bias from that due to other factors. Study Design and Setting An enhancement to the usual funnel plot is proposed that allows the statistical significance of study estimates to be considered. Contour lines indicating conventional milestones in levels of statistical significance (e.g., Results This contour overlay aids the interpretation of the funnel plot. For example, if studies appear to be missing in areas of statistical nonsignificance, then this adds credence to the possibility that the asymmetry is due to Publication Bias. Conversely, if the supposed missing studies are in areas of higher statistical significance, this would suggest the cause of the asymmetry may be more likely to be due to factors other than Publication Bias, such as variable study quality. Conclusions We believe this enhancement to funnel plots (i) is simple to implement, (ii) is widely applicable, (iii) greatly improves interpretability, and (iv) should be used routinely.
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performance of the trim and fill method in the presence of Publication Bias and between study heterogeneity
Statistics in Medicine, 2007Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Alex J Sutton, Lesley RushtonAbstract:The trim and fill method allows estimation of an adjusted meta-analysis estimate in the presence of Publication Bias. To date, the performance of the trim and fill method has had little assessment. In this paper, we provide a more comprehensive examination of different versions of the trim and fill method in a number of simulated meta-analysis scenarios, comparing results with those from usual unadjusted meta-analysis models and two simple alternatives, namely use of the estimate from: (i) the largest; or (ii) the most precise study in the meta-analysis. Findings suggest a great deal of variability in the performance of the different approaches. When there is large between-study heterogeneity the trim and fill method can underestimate the true positive effect when there is no Publication Bias. However, when Publication Bias is present the trim and fill method can give estimates that are less Biased than the usual meta-analysis models. Although results suggest that the use of the estimate from the largest or most precise study seems a reasonable approach in the presence of Publication Bias, when between-study heterogeneity exists our simulations show that these estimates are quite Biased. We conclude that in the presence of Publication Bias use of the trim and fill method can help to reduce the Bias in pooled estimates, even though the performance of this method is not ideal. However, because we do not know whether funnel plot asymmetry is truly caused by Publication Bias, and because there is great variability in the performance of different trim and fill estimators and models in various meta-analysis scenarios, we recommend use of the trim and fill method as a form of sensitivity analysis as intended by the authors of the method.
Lesley Rushton - One of the best experts on this subject based on the ideXlab platform.
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assessing Publication Bias in meta analyses in the presence of between study heterogeneity
Journal of The Royal Statistical Society Series A-statistics in Society, 2010Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Lesley Rushton, Alex J Sutton, Santiago G MorenoAbstract:Summary. Between-study heterogeneity and Publication Bias are common features of a meta-analysis that can be present simultaneously. When both are suspected, consideration must be made of each in the assessment of the other. We consider extended funnel plot tests for detecting Publication Bias, and selection modelling and trim-and-fill methods to adjust for Publication Bias in the presence of between-study heterogeneity. These methods are applied to two example data sets. Results indicate that ignoring between-study heterogeneity when assessing Publication Bias can be misleading, but that methods to test or adjust for Publication Bias in the presence of heterogeneity may not be powerful when the meta-analysis is not large. It is therefore unrealistic to expect to disentangle the effects of Publication Bias and heterogeneity reliably in all except the largest meta-analyses.
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contour enhanced meta analysis funnel plots help distinguish Publication Bias from other causes of asymmetry
Journal of Clinical Epidemiology, 2008Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Alex J Sutton, Lesley RushtonAbstract:Abstract Objectives To present the contour-enhanced funnel plot as an aid to differentiating asymmetry due to Publication Bias from that due to other factors. Study Design and Setting An enhancement to the usual funnel plot is proposed that allows the statistical significance of study estimates to be considered. Contour lines indicating conventional milestones in levels of statistical significance (e.g., Results This contour overlay aids the interpretation of the funnel plot. For example, if studies appear to be missing in areas of statistical nonsignificance, then this adds credence to the possibility that the asymmetry is due to Publication Bias. Conversely, if the supposed missing studies are in areas of higher statistical significance, this would suggest the cause of the asymmetry may be more likely to be due to factors other than Publication Bias, such as variable study quality. Conclusions We believe this enhancement to funnel plots (i) is simple to implement, (ii) is widely applicable, (iii) greatly improves interpretability, and (iv) should be used routinely.
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performance of the trim and fill method in the presence of Publication Bias and between study heterogeneity
Statistics in Medicine, 2007Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Alex J Sutton, Lesley RushtonAbstract:The trim and fill method allows estimation of an adjusted meta-analysis estimate in the presence of Publication Bias. To date, the performance of the trim and fill method has had little assessment. In this paper, we provide a more comprehensive examination of different versions of the trim and fill method in a number of simulated meta-analysis scenarios, comparing results with those from usual unadjusted meta-analysis models and two simple alternatives, namely use of the estimate from: (i) the largest; or (ii) the most precise study in the meta-analysis. Findings suggest a great deal of variability in the performance of the different approaches. When there is large between-study heterogeneity the trim and fill method can underestimate the true positive effect when there is no Publication Bias. However, when Publication Bias is present the trim and fill method can give estimates that are less Biased than the usual meta-analysis models. Although results suggest that the use of the estimate from the largest or most precise study seems a reasonable approach in the presence of Publication Bias, when between-study heterogeneity exists our simulations show that these estimates are quite Biased. We conclude that in the presence of Publication Bias use of the trim and fill method can help to reduce the Bias in pooled estimates, even though the performance of this method is not ideal. However, because we do not know whether funnel plot asymmetry is truly caused by Publication Bias, and because there is great variability in the performance of different trim and fill estimators and models in various meta-analysis scenarios, we recommend use of the trim and fill method as a form of sensitivity analysis as intended by the authors of the method.
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comparison of two methods to detect Publication Bias in meta analysis
JAMA, 2006Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Alex J Sutton, Lesley RushtonAbstract:Results Type I error rates for Egger’s regression test are higher than those for the alternative regression test. The alternative regression test has the appropriate type I error rates regardless of the size of the underlying OR, the number of primary studies in the meta-analysis, and the level of between-study heterogeneity. The alternative regression test has comparable power to Egger’s regression test to detect Publication Bias under conditions of low between-study heterogeneity. Conclusion Because of appropriate type I error rates and reduction in the correlation between the lnOR and its variance, the alternative regression test can be used in place of Egger’s regression test when the summary estimates are lnORs.
Jaime Peters - One of the best experts on this subject based on the ideXlab platform.
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assessing Publication Bias in meta analyses in the presence of between study heterogeneity
Journal of The Royal Statistical Society Series A-statistics in Society, 2010Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Lesley Rushton, Alex J Sutton, Santiago G MorenoAbstract:Summary. Between-study heterogeneity and Publication Bias are common features of a meta-analysis that can be present simultaneously. When both are suspected, consideration must be made of each in the assessment of the other. We consider extended funnel plot tests for detecting Publication Bias, and selection modelling and trim-and-fill methods to adjust for Publication Bias in the presence of between-study heterogeneity. These methods are applied to two example data sets. Results indicate that ignoring between-study heterogeneity when assessing Publication Bias can be misleading, but that methods to test or adjust for Publication Bias in the presence of heterogeneity may not be powerful when the meta-analysis is not large. It is therefore unrealistic to expect to disentangle the effects of Publication Bias and heterogeneity reliably in all except the largest meta-analyses.
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assessment of regression based methods to adjust for Publication Bias through a comprehensive simulation study
BMC Medical Research Methodology, 2009Co-Authors: Santiago G Moreno, Jaime Peters, Keith R Abrams, T D Stanley, Alex J Sutton, A E Ades, Nicola J CooperAbstract:In meta-analysis, the presence of funnel plot asymmetry is attributed to Publication or other small-study effects, which causes larger effects to be observed in the smaller studies. This issue potentially mean inappropriate conclusions are drawn from a meta-analysis. If meta-analysis is to be used to inform decision-making, a reliable way to adjust pooled estimates for potential funnel plot asymmetry is required. A comprehensive simulation study is presented to assess the performance of different adjustment methods including the novel application of several regression-based methods (which are commonly applied to detect Publication Bias rather than adjust for it) and the popular Trim & Fill algorithm. Meta-analyses with binary outcomes, analysed on the log odds ratio scale, were simulated by considering scenarios with and without i) Publication Bias and; ii) heterogeneity. Publication Bias was induced through two underlying mechanisms assuming the probability of Publication depends on i) the study effect size; or ii) the p-value. The performance of all methods tended to worsen as unexplained heterogeneity increased and the number of studies in the meta-analysis decreased. Applying the methods conditional on an initial test for the presence of funnel plot asymmetry generally provided poorer performance than the unconditional use of the adjustment method. Several of the regression based methods consistently outperformed the Trim & Fill estimators. Regression-based adjustments for Publication Bias and other small study effects are easy to conduct and outperformed more established methods over a wide range of simulation scenarios.
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contour enhanced meta analysis funnel plots help distinguish Publication Bias from other causes of asymmetry
Journal of Clinical Epidemiology, 2008Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Alex J Sutton, Lesley RushtonAbstract:Abstract Objectives To present the contour-enhanced funnel plot as an aid to differentiating asymmetry due to Publication Bias from that due to other factors. Study Design and Setting An enhancement to the usual funnel plot is proposed that allows the statistical significance of study estimates to be considered. Contour lines indicating conventional milestones in levels of statistical significance (e.g., Results This contour overlay aids the interpretation of the funnel plot. For example, if studies appear to be missing in areas of statistical nonsignificance, then this adds credence to the possibility that the asymmetry is due to Publication Bias. Conversely, if the supposed missing studies are in areas of higher statistical significance, this would suggest the cause of the asymmetry may be more likely to be due to factors other than Publication Bias, such as variable study quality. Conclusions We believe this enhancement to funnel plots (i) is simple to implement, (ii) is widely applicable, (iii) greatly improves interpretability, and (iv) should be used routinely.
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performance of the trim and fill method in the presence of Publication Bias and between study heterogeneity
Statistics in Medicine, 2007Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Alex J Sutton, Lesley RushtonAbstract:The trim and fill method allows estimation of an adjusted meta-analysis estimate in the presence of Publication Bias. To date, the performance of the trim and fill method has had little assessment. In this paper, we provide a more comprehensive examination of different versions of the trim and fill method in a number of simulated meta-analysis scenarios, comparing results with those from usual unadjusted meta-analysis models and two simple alternatives, namely use of the estimate from: (i) the largest; or (ii) the most precise study in the meta-analysis. Findings suggest a great deal of variability in the performance of the different approaches. When there is large between-study heterogeneity the trim and fill method can underestimate the true positive effect when there is no Publication Bias. However, when Publication Bias is present the trim and fill method can give estimates that are less Biased than the usual meta-analysis models. Although results suggest that the use of the estimate from the largest or most precise study seems a reasonable approach in the presence of Publication Bias, when between-study heterogeneity exists our simulations show that these estimates are quite Biased. We conclude that in the presence of Publication Bias use of the trim and fill method can help to reduce the Bias in pooled estimates, even though the performance of this method is not ideal. However, because we do not know whether funnel plot asymmetry is truly caused by Publication Bias, and because there is great variability in the performance of different trim and fill estimators and models in various meta-analysis scenarios, we recommend use of the trim and fill method as a form of sensitivity analysis as intended by the authors of the method.
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comparison of two methods to detect Publication Bias in meta analysis
JAMA, 2006Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Alex J Sutton, Lesley RushtonAbstract:Results Type I error rates for Egger’s regression test are higher than those for the alternative regression test. The alternative regression test has the appropriate type I error rates regardless of the size of the underlying OR, the number of primary studies in the meta-analysis, and the level of between-study heterogeneity. The alternative regression test has comparable power to Egger’s regression test to detect Publication Bias under conditions of low between-study heterogeneity. Conclusion Because of appropriate type I error rates and reduction in the correlation between the lnOR and its variance, the alternative regression test can be used in place of Egger’s regression test when the summary estimates are lnORs.
Keith R Abrams - One of the best experts on this subject based on the ideXlab platform.
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assessing Publication Bias in meta analyses in the presence of between study heterogeneity
Journal of The Royal Statistical Society Series A-statistics in Society, 2010Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Lesley Rushton, Alex J Sutton, Santiago G MorenoAbstract:Summary. Between-study heterogeneity and Publication Bias are common features of a meta-analysis that can be present simultaneously. When both are suspected, consideration must be made of each in the assessment of the other. We consider extended funnel plot tests for detecting Publication Bias, and selection modelling and trim-and-fill methods to adjust for Publication Bias in the presence of between-study heterogeneity. These methods are applied to two example data sets. Results indicate that ignoring between-study heterogeneity when assessing Publication Bias can be misleading, but that methods to test or adjust for Publication Bias in the presence of heterogeneity may not be powerful when the meta-analysis is not large. It is therefore unrealistic to expect to disentangle the effects of Publication Bias and heterogeneity reliably in all except the largest meta-analyses.
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assessment of regression based methods to adjust for Publication Bias through a comprehensive simulation study
BMC Medical Research Methodology, 2009Co-Authors: Santiago G Moreno, Jaime Peters, Keith R Abrams, T D Stanley, Alex J Sutton, A E Ades, Nicola J CooperAbstract:In meta-analysis, the presence of funnel plot asymmetry is attributed to Publication or other small-study effects, which causes larger effects to be observed in the smaller studies. This issue potentially mean inappropriate conclusions are drawn from a meta-analysis. If meta-analysis is to be used to inform decision-making, a reliable way to adjust pooled estimates for potential funnel plot asymmetry is required. A comprehensive simulation study is presented to assess the performance of different adjustment methods including the novel application of several regression-based methods (which are commonly applied to detect Publication Bias rather than adjust for it) and the popular Trim & Fill algorithm. Meta-analyses with binary outcomes, analysed on the log odds ratio scale, were simulated by considering scenarios with and without i) Publication Bias and; ii) heterogeneity. Publication Bias was induced through two underlying mechanisms assuming the probability of Publication depends on i) the study effect size; or ii) the p-value. The performance of all methods tended to worsen as unexplained heterogeneity increased and the number of studies in the meta-analysis decreased. Applying the methods conditional on an initial test for the presence of funnel plot asymmetry generally provided poorer performance than the unconditional use of the adjustment method. Several of the regression based methods consistently outperformed the Trim & Fill estimators. Regression-based adjustments for Publication Bias and other small study effects are easy to conduct and outperformed more established methods over a wide range of simulation scenarios.
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contour enhanced meta analysis funnel plots help distinguish Publication Bias from other causes of asymmetry
Journal of Clinical Epidemiology, 2008Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Alex J Sutton, Lesley RushtonAbstract:Abstract Objectives To present the contour-enhanced funnel plot as an aid to differentiating asymmetry due to Publication Bias from that due to other factors. Study Design and Setting An enhancement to the usual funnel plot is proposed that allows the statistical significance of study estimates to be considered. Contour lines indicating conventional milestones in levels of statistical significance (e.g., Results This contour overlay aids the interpretation of the funnel plot. For example, if studies appear to be missing in areas of statistical nonsignificance, then this adds credence to the possibility that the asymmetry is due to Publication Bias. Conversely, if the supposed missing studies are in areas of higher statistical significance, this would suggest the cause of the asymmetry may be more likely to be due to factors other than Publication Bias, such as variable study quality. Conclusions We believe this enhancement to funnel plots (i) is simple to implement, (ii) is widely applicable, (iii) greatly improves interpretability, and (iv) should be used routinely.
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performance of the trim and fill method in the presence of Publication Bias and between study heterogeneity
Statistics in Medicine, 2007Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Alex J Sutton, Lesley RushtonAbstract:The trim and fill method allows estimation of an adjusted meta-analysis estimate in the presence of Publication Bias. To date, the performance of the trim and fill method has had little assessment. In this paper, we provide a more comprehensive examination of different versions of the trim and fill method in a number of simulated meta-analysis scenarios, comparing results with those from usual unadjusted meta-analysis models and two simple alternatives, namely use of the estimate from: (i) the largest; or (ii) the most precise study in the meta-analysis. Findings suggest a great deal of variability in the performance of the different approaches. When there is large between-study heterogeneity the trim and fill method can underestimate the true positive effect when there is no Publication Bias. However, when Publication Bias is present the trim and fill method can give estimates that are less Biased than the usual meta-analysis models. Although results suggest that the use of the estimate from the largest or most precise study seems a reasonable approach in the presence of Publication Bias, when between-study heterogeneity exists our simulations show that these estimates are quite Biased. We conclude that in the presence of Publication Bias use of the trim and fill method can help to reduce the Bias in pooled estimates, even though the performance of this method is not ideal. However, because we do not know whether funnel plot asymmetry is truly caused by Publication Bias, and because there is great variability in the performance of different trim and fill estimators and models in various meta-analysis scenarios, we recommend use of the trim and fill method as a form of sensitivity analysis as intended by the authors of the method.
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comparison of two methods to detect Publication Bias in meta analysis
JAMA, 2006Co-Authors: Jaime Peters, David R Jones, Keith R Abrams, Alex J Sutton, Lesley RushtonAbstract:Results Type I error rates for Egger’s regression test are higher than those for the alternative regression test. The alternative regression test has the appropriate type I error rates regardless of the size of the underlying OR, the number of primary studies in the meta-analysis, and the level of between-study heterogeneity. The alternative regression test has comparable power to Egger’s regression test to detect Publication Bias under conditions of low between-study heterogeneity. Conclusion Because of appropriate type I error rates and reduction in the correlation between the lnOR and its variance, the alternative regression test can be used in place of Egger’s regression test when the summary estimates are lnORs.
Erik Von Elm - One of the best experts on this subject based on the ideXlab platform.
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systematic review of the empirical evidence of study Publication Bias and outcome reporting Bias
PLOS ONE, 2008Co-Authors: Kerry Dwan, Douglas G Altman, Juan A Arnaiz, Jill Bloom, Anwen Chan, Eugenia Cronin, Evelyne Decullier, Philippa Easterbrook, Erik Von ElmAbstract:Background The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of Bias that can arise during the completion of a randomised controlled trial. Study Publication Bias has been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making. Until recently, outcome reporting Bias has received less attention.
