Empirical assessment of effect of publication bias on meta-analyses.
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ABSTRACT:
SUBMITTER: Sutton AJ
PROVIDER: S-EPMC27401 | biostudies-literature | 2000 Jun
REPOSITORIES: biostudies-literature
Publications
Empirical assessment of effect of publication bias on meta-analyses.
Sutton A J AJ Duval S J SJ Tweedie R L RL Abrams K R KR Jones D R DR
BMJ (Clinical research ed.) 20000601 7249
<h4>Objective</h4>To assess the effect of publication bias on the results and conclusions of systematic reviews and meta-analyses.<h4>Design</h4>Analysis of published meta-analyses by trim and fill method.<h4>Studies</h4>48 reviews in Cochrane Database of Systematic Reviews that considered a binary endpoint and contained 10 or more individual studies.<h4>Main outcome measures</h4>Number of reviews with missing studies and effect on conclusions of meta-analyses.<h4>Results</h4>The trim and fill f ...[more]
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Project description:IntroductionPositive results have a greater chance of being published and outcomes that are statistically significant have a greater chance of being fully reported. One consequence of research underreporting is that it may influence the sample of studies that is available for a meta-analysis. Smaller studies are often characterized by larger effects in published meta-analyses, which can be possibly explained by publication bias. We investigated the association between the statistical significance of the results and the probability of being included in recent meta-analyses.MethodsFor meta-analyses of clinical trials, we defined the relative risk as the ratio of the probability of including statistically significant results favoring the treatment to the probability of including other results. For meta-analyses of other studies, we defined the relative risk as the ratio of the probability of including biologically plausible statistically significant results to the probability of including other results. We applied a Bayesian selection model for meta-analyses that included at least 30 studies and were published in four major general medical journals (BMJ, JAMA, Lancet, and PLOS Medicine) between 2008 and 2012.ResultsWe identified 49 meta-analyses. The estimate of the relative risk was greater than one in 42 meta-analyses, greater than two in 16 meta-analyses, greater than three in eight meta-analyses, and greater than five in four meta-analyses. In 10 out of 28 meta-analyses of clinical trials, there was strong evidence that statistically significant results favoring the treatment were more likely to be included. In 4 out of 19 meta-analyses of observational studies, there was strong evidence that plausible statistically significant outcomes had a higher probability of being included.ConclusionsPublication bias was present in a substantial proportion of large meta-analyses that were recently published in four major medical journals.
| S-EPMC3868709 | biostudies-literature
Project description:BACKGROUND:Decision makers rely on meta-analytic estimates to trade off benefits and harms. Publication bias impairs the validity and generalizability of such estimates. The performance of various statistical tests for publication bias has been largely compared using simulation studies and has not been systematically evaluated in empirical data. METHODS:This study compares seven commonly used publication bias tests (i.e., Begg's rank test, trim-and-fill, Egger's, Tang's, Macaskill's, Deeks', and Peters' regression tests) based on 28,655 meta-analyses available in the Cochrane Library. RESULTS:Egger's regression test detected publication bias more frequently than other tests (15.7% in meta-analyses of binary outcomes and 13.5% in meta-analyses of non-binary outcomes). The proportion of statistically significant publication bias tests was greater for larger meta-analyses, especially for Begg's rank test and the trim-and-fill method. The agreement among Tang's, Macaskill's, Deeks', and Peters' regression tests for binary outcomes was moderately strong (most ?'s were around 0.6). Tang's and Deeks' tests had fairly similar performance (??>?0.9). The agreement among Begg's rank test, the trim-and-fill method, and Egger's regression test was weak or moderate (? <?0.5). CONCLUSIONS:Given the relatively low agreement between many publication bias tests, meta-analysts should not rely on a single test and may apply multiple tests with various assumptions. Non-statistical approaches to evaluating publication bias (e.g., searching clinical trials registries, records of drug approving agencies, and scientific conference proceedings) remain essential.
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Project description:We propose sensitivity analyses for publication bias in meta-analyses. We consider a publication process such that 'statistically significant' results are more likely to be published than negative or "non-significant" results by an unknown ratio, η. Our proposed methods also accommodate some plausible forms of selection based on a study's standard error. Using inverse probability weighting and robust estimation that accommodates non-normal population effects, small meta-analyses, and clustering, we develop sensitivity analyses that enable statements such as 'For publication bias to shift the observed point estimate to the null, "significant" results would need to be at least 30 fold more likely to be published than negative or "non-significant" results'. Comparable statements can be made regarding shifting to a chosen non-null value or shifting the confidence interval. To aid interpretation, we describe empirical benchmarks for plausible values of η across disciplines. We show that a worst-case meta-analytic point estimate for maximal publication bias under the selection model can be obtained simply by conducting a standard meta-analysis of only the negative and 'non-significant' studies; this method sometimes indicates that no amount of such publication bias could 'explain away' the results. We illustrate the proposed methods by using real meta-analyses and provide an R package: PublicationBias.
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Project description:BACKGROUND: Despite efforts to assure high methodological standards, systematic reviews may be affected by publication bias. The objective of this study was to evaluate the occurrence of publication bias in a collection of high quality systematic reviews on reproductive health. METHODS: Systematic reviews included in the Reproductive Health Library (RHL), issue No 9, were assessed. Funnel plot was used to assess meta-analyses containing 10 or more trials reporting a binary outcome. A funnel plot, the estimated number of missing studies and the adjusted combined effect size were obtained using the "trim and fill method". Meta-analyses results that were not considered to be robust due to a possible publication bias were submitted to a more detailed assessment. RESULTS: A total of 21 systematic reviews were assessed. The number of trials comprising each one ranged from 10 to 83 (median = 13), totaling 379 trials, whose results have been summarized. None of the reviews had reported any evaluation of publication bias or funnel plot asymmetry. Some degree of asymmetry in funnel plots was observed in 18 of the 21 meta-analyses evaluated (85.7%), with the estimated number of missing studies ranging from 1 to 18 (median = 3). Only for three meta-analyses, the conclusion could not be considered robust due to a possible publication bias. CONCLUSION: Asymmetry is a frequent finding in funnel plots of meta-analyses in reproductive health, but according to the present evaluation, less than 15% of meta-analyses report conclusions that would not be considered robust. Publication bias and other sources of asymmetry in funnel plots should be systematically addressed by reproductive health meta-analysts. Next amendments in Cochrane systematic reviews should include this type of evaluation. Further studies regarding the evolution of effect size and publication bias over time in systematic reviews in reproductive health are needed.
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Project description:Because negative findings have less chance of getting published, available studies tend to be a biased sample. This leads to an inflation of effect size estimates to an unknown degree. To see how meta-analyses in education account for publication bias, we surveyed all meta-analyses published in the last five years in the Review of Educational Research and Educational Research Review. The results show that meta-analyses usually neglect publication bias adjustment. In the minority of meta-analyses adjusting for bias, mostly non-principled adjustment methods were used, and only rarely were the conclusions based on corrected estimates, rendering the adjustment inconsequential. It is argued that appropriate state-of-the-art adjustment (e.g., selection models) should be attempted by default, yet one needs to take into account the uncertainty inherent in any meta-analytic inference under bias. We conclude by providing practical recommendations on dealing with publication bias.
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Project description:Publication bias is a substantial problem for the credibility of research in general and of meta-analyses in particular, as it yields overestimated effects and may suggest the existence of non-existing effects. Although there is consensus that publication bias exists, how strongly it affects different scientific literatures is currently less well-known. We examined evidence of publication bias in a large-scale data set of primary studies that were included in 83 meta-analyses published in Psychological Bulletin (representing meta-analyses from psychology) and 499 systematic reviews from the Cochrane Database of Systematic Reviews (CDSR; representing meta-analyses from medicine). Publication bias was assessed on all homogeneous subsets (3.8% of all subsets of meta-analyses published in Psychological Bulletin) of primary studies included in meta-analyses, because publication bias methods do not have good statistical properties if the true effect size is heterogeneous. Publication bias tests did not reveal evidence for bias in the homogeneous subsets. Overestimation was minimal but statistically significant, providing evidence of publication bias that appeared to be similar in both fields. However, a Monte-Carlo simulation study revealed that the creation of homogeneous subsets resulted in challenging conditions for publication bias methods since the number of effect sizes in a subset was rather small (median number of effect sizes equaled 6). Our findings are in line with, in its most extreme case, publication bias ranging from no bias until only 5% statistically nonsignificant effect sizes being published. These and other findings, in combination with the small percentages of statistically significant primary effect sizes (28.9% and 18.9% for subsets published in Psychological Bulletin and CDSR), led to the conclusion that evidence for publication bias in the studied homogeneous subsets is weak, but suggestive of mild publication bias in both psychology and medicine.
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Project description:To explore whether systematic reviewers selectively include trial effect estimates in meta-analyses when multiple are available, and what impact this may have on meta-analytic effects.Cross-sectional study.We randomly selected systematic reviews of interventions from 2 clinical specialties published between January 2010 and 2012. The first presented meta-analysis of a continuous outcome in each review was selected (index meta-analysis), and all trial effect estimates that were eligible for inclusion in the meta-analysis (eg, from multiple scales or time points) were extracted from trial reports.We calculated a statistic (the Potential Bias Index (PBI)) to quantify and test for evidence of selective inclusion. The PBI ranges from 0 to 1; values above or below 0.5 are suggestive of selective inclusion of effect estimates more or less favourable to the intervention, respectively. The impact of any potential selective inclusion was investigated by comparing the index meta-analytic standardised mean difference (SMD) to the median of a randomly constructed distribution of meta-analytic SMDs (representing the meta-analytic SMD expected when there is no selective inclusion).31 reviews (250 trials) were included. The estimated PBI was 0.57 (95% CI 0.50 to 0.63), suggesting that trial effect estimates that were more favourable to the intervention were included in meta-analyses slightly more often than expected under a process consistent with random selection; however, the 95% CI included the null hypothesis of no selective inclusion. Any potential selective inclusion did not have an important impact on the meta-analytic effects.There was no clear evidence that selective inclusion of trial effect estimates occurred in this sample of meta-analyses. Further research on selective inclusion in other clinical specialties is needed. To enable readers to assess the risk of selective inclusion bias, we recommend that systematic reviewers report the methods used to select effect estimates to include in meta-analyses.
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Project description:The validity of research synthesis is threatened if published studies comprise a biased selection of all studies that have been conducted. We conducted a meta-analysis to ascertain the strength and consistency of the association between study results and formal publication.The Cochrane Methodology Register Database, MEDLINE and other electronic bibliographic databases were searched (to May 2009) to identify empirical studies that tracked a cohort of studies and reported the odds of formal publication by study results. Reference lists of retrieved articles were also examined for relevant studies. Odds ratios were used to measure the association between formal publication and significant or positive results. Included studies were separated into subgroups according to starting time of follow-up, and results from individual cohort studies within the subgroups were quantitatively pooled.We identified 12 cohort studies that followed up research from inception, four that included trials submitted to a regulatory authority, 28 that assessed the fate of studies presented as conference abstracts, and four cohort studies that followed manuscripts submitted to journals. The pooled odds ratio of publication of studies with positive results, compared to those without positive results (publication bias) was 2.78 (95% CI: 2.10 to 3.69) in cohorts that followed from inception, 5.00 (95% CI: 2.01 to 12.45) in trials submitted to regulatory authority, 1.70 (95% CI: 1.44 to 2.02) in abstract cohorts, and 1.06 (95% CI: 0.80 to 1.39) in cohorts of manuscripts.Dissemination of research findings is likely to be a biased process. Publication bias appears to occur early, mainly before the presentation of findings at conferences or submission of manuscripts to journals.
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Project description: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.We review and summarise the evidence from a series of cohort studies that have assessed study publication bias and outcome reporting bias in randomised controlled trials. Sixteen studies were eligible of which only two followed the cohort all the way through from protocol approval to information regarding publication of outcomes. Eleven of the studies investigated study publication bias and five investigated outcome reporting bias. Three studies have found that statistically significant outcomes had a higher odds of being fully reported compared to non-significant outcomes (range of odds ratios: 2.2 to 4.7). In comparing trial publications to protocols, we found that 40-62% of studies had at least one primary outcome that was changed, introduced, or omitted. We decided not to undertake meta-analysis due to the differences between studies.Recent work provides direct empirical evidence for the existence of study publication bias and outcome reporting bias. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials.
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