The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Richard D Riley - One of the best experts on this subject based on the ideXlab platform.
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galaxy Plot a new visualization tool of bivariate meta analysis studies
American Journal of Epidemiology, 2020Co-Authors: Chuan Hong, Richard D Riley, Rui Duan, Lingzhen Zeng, Rebecca A Hubbard, Thomas Lumley, Stephen E Kimmel, Yong ChenAbstract:Funnel Plots have been widely used to detect small study effects in the results of univariate meta-analyses. However, there is no existing visualization tool that is the counterpart of the Funnel Plot in the multivariate setting. We propose a new visualization method, the galaxy Plot, which can simultaneously present the effect sizes of bivariate outcomes and their standard errors in a two-dimensional space. We illustrate the use of galaxy Plot by two case studies, including a meta-analysis of hypertension trials with studies from 1979 to 1991, and a meta-analysis of structured telephone support or non-invasive telemonitoring with studies from 1966 to 2015. The galaxy Plot is an intuitive visualization tool that can aid in interpretation of results of multivariate meta-analysis. It preserves all of the information presented by separate Funnel Plots for each outcome while elucidating more complex features that may only be revealed by examining the joint distribution of the bivariate outcomes.
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detecting small study effects and Funnel Plot asymmetry in meta analysis of survival data a comparison of new and existing tests
Research Synthesis Methods, 2018Co-Authors: Thomas P A Debray, Karel G M Moons, Richard D RileyAbstract:Small-study effects are a common threat in systematic reviews and may indicate publication bias. Their existence is often verified by visual inspection of the Funnel Plot. Formal tests to assess the presence of Funnel Plot asymmetry typically estimate the association between the reported effect size and their standard error, the total sample size, or the inverse of the total sample size. In this paper, we demonstrate that the application of these tests may be less appropriate in meta-analysis of survival data, where censoring influences statistical significance of the hazard ratio. We subsequently propose 2 new tests that are based on the total number of observed events and adopt a multiplicative variance component. We compare the performance of the various Funnel Plot asymmetry tests in an extensive simulation study where we varied the true hazard ratio (0.5 to 1), the number of published trials (N=10 to 100), the degree of censoring within trials (0% to 90%), and the mechanism leading to participant dropout (noninformative versus informative). Results demonstrate that previous well-known tests for detecting Funnel Plot asymmetry suffer from low power or excessive type-I error rates in meta-analysis of survival data, particularly when trials are affected by participant dropout. Because our novel test (adopting estimates of the asymptotic precision as study weights) yields reasonable power and maintains appropriate type-I error rates, we recommend its use to evaluate Funnel Plot asymmetry in meta-analysis of survival data. The use of Funnel Plot asymmetry tests should, however, be avoided when there are few trials available for any meta-analysis.
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Assessment of publication bias, selection bias, and unavailable data in meta-analyses using individual participant data: a database survey
BMJ (Clinical research ed.), 2012Co-Authors: Ikhlaaq Ahmed, Alex J. Sutton, Richard D RileyAbstract:Objective To examine the potential for publication bias, data availability bias, and reviewer selection bias in recently published meta-analyses that use individual participant data and to investigate whether authors of such meta-analyses seemed aware of these issues. Design In a database of 383 meta-analyses of individual participant data that were published between 1991 and March 2009, we surveyed the 31 most recent meta-analyses of randomised trials that examined whether an intervention was effective. Identification of relevant articles and data extraction was undertaken by one author and checked by another. Results Only nine (29%) of the 31 meta-analyses included individual participant data from “grey literature” (such as unpublished studies) in their primary meta-analysis, and the potential for publication bias was discussed or investigated in just 10 (32%). Sixteen (52%) of the 31 meta-analyses did not obtain all the individual participant data requested, yet five of these (31%) did not mention this as a potential limitation, and only six (38%) examined how trials without individual participant data might affect the conclusions. In nine (29%) of the meta-analyses reviewer selection bias was a potential issue, as the identification of relevant trials was either not stated or based on a more selective, non-systematic approach. Investigation of four meta-analyses containing data from ≥10 trials revealed one with an asymmetric Funnel Plot consistent with publication bias, and the inclusion of studies without individual participant data revealed additional heterogeneity between trials. Conclusions Publication, availability, and selection biases are a potential concern for meta-analyses of individual participant data, but many reviewers neglect to examine or discuss them. These issues warn against uncritically viewing any meta-analysis that uses individual participant data as the most reliable. Reviewers should seek individual participant data from all studies identified by a systematic review; include, where possible, aggregate data from any studies lacking individual participant data to consider their potential impact; and investigate Funnel Plot asymmetry in line with recent guidelines.
Dongfeng Zhang - One of the best experts on this subject based on the ideXlab platform.
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dietary magnesium and calcium intake and risk of depression in the general population a meta analysis
Australian and New Zealand Journal of Psychiatry, 2017Co-Authors: Weijing Wang, Dongfeng ZhangAbstract:Objective:Several epidemiological studies have evaluated the associations between dietary magnesium (Mg) and calcium (Ca) intake and the risk of depression. However, the results of these studies remain controversial. Thus, we performed a meta-analysis to explore these associations and to investigate the possible dose–response relationship between dietary Mg intake and risk of depression.Methods:MEDLINE, Web of Science, Embase, Cochrane CENTRAL, CINAHL database, Chinese National Knowledge Infrastructure, Wan fang databases and Databases of Chinese Scientific and Technical Periodicals were searched for eligible publications up to September 2016. Pooled relative risks with 95% confidence intervals were calculated using random-effects model. Publication bias was estimated using Egger’s test and the Funnel Plot. Dose–response relationship was assessed by restricted cubic spline functions.Results:A total of 17 epidemiological studies from 12 articles were included in the present meta-analysis. Among these studi...
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fruit and vegetable consumption and the risk of depression a meta analysis
Nutrition, 2016Co-Authors: Xiaoqin Liu, Ying Yan, Dongfeng ZhangAbstract:Abstract Objective Epidemiologic investigations evaluating the association of fruit and vegetable consumption with depression risk have yielded controversial results. Therefore, a meta-analysis was carried out to qualitatively summarize the evidence regarding association of fruit and vegetable intake with risk of depression in the general population. Methods PubMed, Embase, and Web of Knowledge were searched for relevant articles published up to June 2015. To evaluate the association of fruit and vegetable intake with depression risk, combined relative risks were calculated with the fixed or random effects model. Meta-regression was conducted to explore potential sources of heterogeneity. Publication bias was estimated by the Egger's test and the Funnel Plot. Results Ten studies involving 227 852 participants for fruit intake and eight studies involving 218 699 participants for vegetable intake were finally included in this study. The combined relative risk (95% confidence interval) of depression for the highest versus lowest category of fruit and vegetable intake was 0.86 (0.81, 0.91; P Conclusions This meta-analysis indicated that fruit and vegetable consumption might be inversely associated with the risk of depression, respectively.
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fish consumption and risk of depression a meta analysis
Journal of Epidemiology and Community Health, 2016Co-Authors: Xiaoqin Liu, Dongfeng ZhangAbstract:Background The association between fish consumption and risk of depression is controversial. We performed a meta-analysis to evaluate the association. Methods A literature search was performed in PubMed, EMBASE and Web of Science database for all relevant studies up to March 2015. We pooled the relative risks (RRs) with 95% CIs from individual studies with random effects model, and conducted meta-regression to explore potential sources of heterogeneity. Publication bias was estimated by Egger9s test and the Funnel Plot. Results A total of 26 studies involving 150 278 participants were included in the present meta-analysis. The pooled RR of depression for the highest versus lowest consumption of fish was 0.83 (95% CI 0.74 to 0.93). The findings remained significant in the cohort studies (RR=0.84, 95% CI 0.75 to 0.94, n=10) as well as in the cross-sectional studies (RR=0.82, 95% CI 0.68 to 1.00, n=16). When men and women were analysed separately, a significant inverse association was also observed. There was no evidence of publication bias. Conclusions This meta-analysis indicates that high-fish consumption can reduce the risk of depression.
Thomas P A Debray - One of the best experts on this subject based on the ideXlab platform.
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detecting small study effects and Funnel Plot asymmetry in meta analysis of survival data a comparison of new and existing tests
Research Synthesis Methods, 2018Co-Authors: Thomas P A Debray, Karel G M Moons, Richard D RileyAbstract:Small-study effects are a common threat in systematic reviews and may indicate publication bias. Their existence is often verified by visual inspection of the Funnel Plot. Formal tests to assess the presence of Funnel Plot asymmetry typically estimate the association between the reported effect size and their standard error, the total sample size, or the inverse of the total sample size. In this paper, we demonstrate that the application of these tests may be less appropriate in meta-analysis of survival data, where censoring influences statistical significance of the hazard ratio. We subsequently propose 2 new tests that are based on the total number of observed events and adopt a multiplicative variance component. We compare the performance of the various Funnel Plot asymmetry tests in an extensive simulation study where we varied the true hazard ratio (0.5 to 1), the number of published trials (N=10 to 100), the degree of censoring within trials (0% to 90%), and the mechanism leading to participant dropout (noninformative versus informative). Results demonstrate that previous well-known tests for detecting Funnel Plot asymmetry suffer from low power or excessive type-I error rates in meta-analysis of survival data, particularly when trials are affected by participant dropout. Because our novel test (adopting estimates of the asymptotic precision as study weights) yields reasonable power and maintains appropriate type-I error rates, we recommend its use to evaluate Funnel Plot asymmetry in meta-analysis of survival data. The use of Funnel Plot asymmetry tests should, however, be avoided when there are few trials available for any meta-analysis.
Jonathan J Deeks - One of the best experts on this subject based on the ideXlab platform.
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the performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed
Journal of Clinical Epidemiology, 2005Co-Authors: Jonathan J Deeks, Petra MacaskillAbstract:Abstract Background and Objective Publication bias and other sample size effects are issues for meta-analyses of test accuracy, as for randomized trials. We investigate limitations of standard Funnel Plots and tests when applied to meta-analyses of test accuracy and look for improved methods. Methods Type I and type II error rates for existing and alternative tests of sample size effects were estimated and compared in simulated meta-analyses of test accuracy. Results Type I error rates for the Begg, Egger, and Macaskill tests are inflated for typical diagnostic odds ratios (DOR), when disease prevalence differs from 50% and when thresholds favor sensitivity over specificity or vice versa. Regression and correlation tests based on functions of effective sample size are valid, if occasionally conservative, tests for sample size effects. Empirical evidence suggests that they have adequate power to be useful tests. When DORs are heterogeneous, however, all tests of Funnel Plot asymmetry have low power. Conclusion Existing tests that use standard errors of odds ratios are likely to be seriously misleading if applied to meta-analyses of test accuracy. The effective sample size Funnel Plot and associated regression test of asymmetry should be used to detect publication bias and other sample size related effects.
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systematic review antacids h2 receptor antagonists prokinetics bismuth and sucralfate therapy for non ulcer dyspepsia
Alimentary Pharmacology & Therapeutics, 2003Co-Authors: Paul Moayyedi, Jonathan J Deeks, S Soo, D Forman, Adam Harris, Michael Innes, Brendan DelaneyAbstract:Summary Background: Evidence for the effectiveness of antacids, histamine-2 receptor antagonists, bismuth salts, sucralfate and prokinetic therapy in non-ulcer dyspepsia is conflicting. Aim: To conduct a systematic review evaluating these therapies in non-ulcer dyspepsia. Methods: Electronic searches were performed using the Cochrane Controlled Trials Register, Medline, EMBASE, Cinahl and SIGLE until September 2002. Dyspepsia outcomes were dichotomized into cured/improved vs. same/worse. Results: Prokinetics [14 trials, 1053 patients; relative risk reduction (RRR), 48%; 95% confidence interval (95% CI), 27–63%] and histamine-2 receptor antagonists (11 trials, 2164 patients; RRR, 22%; 95% CI, 7–35%) were significantly more effective than placebo. Bismuth salts (RRR, 40%; 95% CI, − 3% to 65%) were superior to placebo, but this was of marginal statistical significance. Antacids and sucralfate were not statistically significantly superior to placebo. A Funnel Plot suggested that the prokinetic and histamine-2 receptor antagonist results could be due to publication bias. Conclusions: The meta-analyses suggest that histamine-2 receptor antagonists and prokinetics are superior to placebo. These data are difficult to interpret, however, as Funnel Plot asymmetry suggests that the magnitude of the effect could be due to publication bias or other heterogeneity-related issues.
Jonathan A C Sterne - One of the best experts on this subject based on the ideXlab platform.
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recommendations for examining and interpreting Funnel Plot asymmetry in meta analyses of randomised controlled trials
BMJ, 2011Co-Authors: Jonathan A C Sterne, Alex J. Sutton, John P A Ioannidis, Norma Terrin, David R Jones, Joseph Lau, James R Carpenter, Gerta Rucker, Roger M Harbord, Christopher H SchmidAbstract:Funnel Plots, and tests for Funnel Plot asymmetry, have been widely used to examine bias in the results of meta-analyses. Funnel Plot asymmetry should not be equated with publication bias, because it has a number of other possible causes. This article describes how to interpret Funnel Plot asymmetry, recommends appropriate tests, and explains the implications for choice of meta-analysis model
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a modified test for small study effects in meta analyses of controlled trials with binary endpoints
Statistics in Medicine, 2006Co-Authors: Roger M Harbord, Matthias Egger, Jonathan A C SterneAbstract:Publication bias and related bias in meta-analysis is often examined by visually checking for asymmetry in Funnel Plots of treatment effect against its standard error. Formal statistical tests of Funnel Plot asymmetry have been proposed, but when applied to binary outcome data these can give false-positive rates that are higher than the nominal level in some situations (large treatment effects, or few events per trial, or all trials of similar sizes). We develop a modified linear regression test for Funnel Plot asymmetry based on the efficient score and its variance, Fisher's information. The performance of this test is compared to the other proposed tests in simulation analyses based on the characteristics of published controlled trials. When there is little or no between-trial heterogeneity, this modified test has a false-positive rate close to the nominal level while maintaining similar power to the original linear regression test ('Egger' test). When the degree of between-trial heterogeneity is large, none of the tests that have been proposed has uniformly good properties.
