scholarly journals Sensitivity analysis for publication bias in meta-analyses

2019 ◽  
Author(s):  
Maya B Mathur ◽  
Tyler VanderWeele
Keyword(s):  
Publication Bias ◽  
Normal Population ◽  
Meta Analysis ◽  
Real Life ◽  
R Package ◽  
Sensitivity Analyses ◽  
Point Estimate ◽  
Worst Case ◽  
Null Value ◽  
Meta Analyses

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 "nonsignificant" results by an unknown ratio, eta. 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 'nonsignificant' 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 eta 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 "nonsignificant" studies; this method sometimes indicates that no amount of such publication bias could "explain away" the results. We illustrate the proposed methods using real-life meta-analyses and provide an R package, PublicationBias.

scholarly journals Leishmanicidal effects of resveratrol and its derivatives: a systematic review and meta-Analysis

2020 ◽  
Author(s):  
Nasrin Amiri Dashatan ◽  
Marzieh Ashrafmansouri ◽  
Mehdi Koushki ◽  
Nayebali Ahmadi
Keyword(s):  
Systematic Review ◽  
Publication Bias ◽  
Data Extraction ◽  
Meta Analysis ◽  
Sensitivity Analyses ◽  
Protective Effects ◽  
Random Effects Models ◽  
Important Health ◽  
Mean Differences ◽  
Meta Analyses

Abstract Background Leishmaniasis is one of the most important health problems worldwide. The evidence has suggested that resveratrol and its derivatives have anti-leishmanial effects; however, the results are inconsistent and inconclusive. The aim of this study was to assess the effect of resveratrol and its derivatives on the Leishmania viability through a systematic review and meta-analysis of available relevant studies. Methods The electronic databases PubMed, ScienceDirect, Embase, Web of Science and Scopus were queried between October 2000 and April 2020 using a comprehensive search strategy. The eligible articles selected and data extraction conducted by two reviewers. Mean differences of IC50 (concentration leading to reduction of 50% of Leishmania) for each outcome was calculated using random-effects models. Sensitivity analyses and prespecified subgroup were conducted to evaluate potential heterogeneity and the stability of the pooled results. Publication bias was evaluated using the Egger’s and Begg’s tests. We also followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines for this review. Results Ten studies were included in the meta-analysis. We observed that RSV and its derivatives had significant reducing effects on Leishmania viability in promastigote [24.02 µg/ml; (95% CI 17.1, 30.8); P < 0.05; I2 = 99.8%; P heterogeneity = 0.00] and amastigote [18.3 µg/ml; (95% CI 13.5, 23.2); P < 0.05; I2 = 99.6%; P heterogeneity = 0.00] stages of Leishmania. A significant publication bias was observed in the meta-analysis. Sensitivity analyses showed a similar effect size while reducing the heterogeneity. Subgroup analysis indicated that the pooled effects of leishmanicidal of resveratrol and its derivatives were affected by type of stilbenes and Leishmania species. Conclusions Our findings clearly suggest that the strategies for the treatment of leishmaniasis should be focused on natural products such as RSV and its derivatives. Further study is needed to identify the mechanisms mediating this protective effects of RSV and its derivatives in leishmaniasis.

scholarly journals Estimating publication bias in meta-analyses of peer-reviewed studies: A meta-meta-analysis across disciplines and journal tiers

2019 ◽  
Author(s):  
Maya B Mathur ◽  
Tyler VanderWeele
Keyword(s):  
Publication Bias ◽  
Developmental Psychology ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Open Data ◽  
Scientific Discipline ◽  
Point Estimate ◽  
Medical Journals ◽  
Lower Tier ◽  
Meta Analyses

Selective publication and reporting in individual papers compromise the scientific record, but are meta-analyses as compromised as their constituent studies? We systematically sampled 63 meta-analyses (each comprising at least 40 studies) in PLOS One, top medical journals, top psychology journals, and Metalab, an online, open-data database of developmental psychology meta-analyses. We empirically estimated publication bias in each. Across all meta-analyses, “statistically significant” results in the expected direction were only 1.17 times more likely to be published than “nonsignificant” results or those in the unexpected direction (95%CI: [0.94, 1.47]), with a confidence interval substantially overlapping the null. Comparable estimates were 0.83 for meta-analyses in PLOS One, 1.02 for top medical journals, 1.54 for top psychology journals, and 4.70 for Metalab. The severity of publication bias did differ across individual meta-analyses; in a small minority (10%; 95% CI: [2%, 21%]), publication bias appeared to favor "significant" results in the expected direction by more than 3-fold. We estimated that for 89% of meta-analyses, the amount of publication bias that would be required to attenuate the point estimate to the null exceeded the amount of publication estimated to be actually present in the vast majority of meta-analyses from the relevant scientific discipline (exceeding the 95th percentile of publication bias). Study-level measures (“statistical significance” with a point estimate in the expected direction and point estimate size) did not indicate more publication bias in higher-tier versus lower-tier journals, nor in the earliest studies published on a topic versus later studies. Overall, the mere act of performing a meta-analysis with a large number of studies (at least 40) and that includes non-headline results may largely mitigate publication bias in meta-analyses, suggesting optimism about the validity of meta-analytic results.

scholarly journals How to perform a meta-analysis with R: a practical tutorial

2019 ◽  
Vol 22 (4) ◽  
pp. 153-160 ◽  
Author(s):  
Sara Balduzzi ◽  
Gerta Rücker ◽  
Guido Schwarzer
Keyword(s):  
Mental Health ◽  
Meta Analysis ◽  
R Package ◽  
Outcome Data ◽  
Sensitivity Analyses ◽  
Binary Outcome ◽  
Software Environment ◽  
Flexible Tool ◽  
Funnel Plot ◽  
Meta Analyses

ObjectiveMeta-analysis is of fundamental importance to obtain an unbiased assessment of the available evidence. In general, the use of meta-analysis has been increasing over the last three decades with mental health as a major research topic. It is then essential to well understand its methodology and interpret its results. In this publication, we describe how to perform a meta-analysis with the freely available statistical software environment R, using a working example taken from the field of mental health.MethodsR package meta is used to conduct standard meta-analysis. Sensitivity analyses for missing binary outcome data and potential selection bias are conducted with R package metasens. All essential R commands are provided and clearly described to conduct and report analyses.ResultsThe working example considers a binary outcome: we show how to conduct a fixed effect and random effects meta-analysis and subgroup analysis, produce a forest and funnel plot and to test and adjust for funnel plot asymmetry. All these steps work similar for other outcome types.ConclusionsR represents a powerful and flexible tool to conduct meta-analyses. This publication gives a brief glimpse into the topic and provides directions to more advanced meta-analysis methods available in R.

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews &amp; Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

scholarly journals Risk of dementia in gout and hyperuricaemia: a meta-analysis of cohort studies

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e041680
Author(s):  
Shu-Yue Pan ◽  
Rui-Juan Cheng ◽  
Zi-Jing Xia ◽  
Qiu-Ping Zhang ◽  
Yi Liu
Keyword(s):  
Cohort Studies ◽  
Quality Assessment ◽  
Publication Bias ◽  
Data Extraction ◽  
Meta Analysis ◽  
Sensitivity Analyses ◽  
Cochrane Library ◽  
Crystal Formation ◽  
Medical Subject Headings ◽  
Eligibility Criteria

ObjectivesGout, characterised by hyperuricaemia with monosodium urate crystal formation and inflammation, is the most common inflammatory arthritis in adults. Recent studies have found that elevated uric acid levels are related to the occurrence of dementia. We conducted a study to investigate the association between dementia and gout or hyperuricaemia.DesignSystematic review and meta-analysis of cohort studies.Data sourcesStudies were screened from inception to 28 June 2019 by searching Medline, Embase and the Cochrane Library databases.Eligibility criteriaCohort studies comparing the risk of dementia in patients with gout and hyperuricaemia versus non-gout and non-hyperuricaemia controls were enrolled.Data extraction and analysisTwo reviewers separately selected studies and extracted data using the Medical Subject Headings without restriction on languages or countries. The adjusted HRs were pooled using the DerSimonian and Laird random effects model. Sensitivity analyses were conducted to evaluate the stability of the results. Publication bias was evaluated using Egger’s and Begg’s tests. Quality assessment was performed according to the Newcastle-Ottawa Scale.ResultsFour cohort studies that met the inclusion criteria were included in our meta-analysis. We found that gout and hyperuricaemia did not increase the risk of dementia, with a pooled HR of 0.94 (95% CI 0.69 to 1.28), but might decrease the risk of Alzheimer’s disease (AD), with a pooled HR of 0.78 (95% CI 0.64 to 0.95). There was little evidence of publication bias. Quality assessment of the included studies was high (range: 6–8 points).ConclusionsOur study shows that gout and hyperuricaemia do not increase the risk of dementia. However, gout and hyperuricaemia might have a protective effect against AD. Due to the limited number of research articles, more investigations are needed to demonstrate the potential relationship between dementia and gout or hyperuricaemia.

Use of Statins and Breast Cancer: A Meta-Analysis of Seven Randomized Clinical Trials and Nine Observational Studies

2005 ◽  
Vol 23 (34) ◽  
pp. 8606-8612 ◽  
Author(s):  
Stefanos Bonovas ◽  
Kalitsa Filioussi ◽  
Nikolaos Tsavaris ◽  
Nikolaos M. Sitaras
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Publication Bias ◽  
Observational Studies ◽  
Fixed Effects ◽  
Randomized Clinical Trials ◽  
Meta Analysis ◽  
Sensitivity Analyses ◽  
Fixed Effects Model

Purpose A growing body of evidence suggests that statins may have chemopreventive potential against breast cancer. Laboratory studies demonstrate that statins induce apoptosis and reduce cell invasiveness in various cell lines, including breast carcinoma cells. However, the clinical relevance of these data remains unclear. The nonconclusive nature of the epidemiologic data prompted us to conduct a detailed meta-analysis of the studies published on the subject in peer-reviewed literature. Patients and Methods A comprehensive search for articles published up until 2005 was performed; reviews of each study were conducted; and data were abstracted. Before meta-analysis, the studies were evaluated for publication bias and heterogeneity. Pooled relative risk (RR) estimates and 95% CIs were calculated using the random and the fixed-effects models. Subgroup and sensitivity analyses were also performed. Results Seven large randomized trials and nine observational studies (five case-control and four cohort studies) contributed to the analysis. We found no evidence of publication bias or heterogeneity among the studies. Statin use did not significantly affect breast cancer risk (fixed effects model: RR = 1.03; 95% CI, 0.93 to 1.14; random effects model: RR = 1.02; 95% CI, 0.89 to 1.18). When the analyses were stratified into subgroups, there was no evidence that study design substantially influenced the estimate of effects. Furthermore, the sensitivity analysis confirmed the stability of our results. Conclusion Our meta-analysis findings do not support a protective effect of statins against breast cancer. However, this conclusion is limited by the relatively short follow-up times of the studies analyzed. Further studies are required to investigate the potential decrease in breast cancer risk among long-term statin users.

scholarly journals Comparative efficacy and safety of oral or transdermal opioids in the treatment of knee or hip osteoarthritis: a systematic review and Bayesian network meta-analysis protocol

BMJ Open ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. e022142
Author(s):  
Jun Wang ◽  
Yin Wang ◽  
Hui Zhang ◽  
Ming Lu ◽  
Weilu Gao ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Meta Analysis ◽  
Hip Osteoarthritis ◽  
Sensitivity Analyses ◽  
Cochrane Library ◽  
Efficacy And Safety ◽  
Meta Analyses ◽  
Transdermal Opioids ◽  
And Function

IntroductionOsteoarthritis is a common degenerative joint disease that eventually leads to disability and poor quality of life. The main symptoms are joint pain and mobility disorders. If the patient has severe pain or other analgesics are contraindicated, opioids may be a viable treatment option. To evaluate and compare the efficacy and safety of opioids in the treatment of knee or hip osteoarthritis, we will integrate direct and indirect evidence using a Bayesian network meta-analysis to establish hierarchies of these drugs.Methods and analysisWe will search the Medical Literature Analysis and Retrieval System Online, Excerpta Medica database, Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, Web of Science and PsycINFO databases as well as published and unpublished research in international registries and regulatory agency websites for osteoarthritis reports published prior to 5 January 2018. There will be no restrictions on the language. Randomised clinical trials that compare oral or transdermal opioids with other various opioids, placebo or no treatment for patients with knee or hip osteoarthritis will be included. The primary outcomes of efficacy will be pain and function. We will use pain and function scales to evaluate the main outcomes. The secondary outcomes of safety will be defined as the proportion of patients who have stopped treatment due to side effects. Pairwise meta-analyses and Bayesian network meta-analyses will be performed for all related outcome measures. We will conduct subgroup analyses and sensitivity analyses to assess the robustness of our findings. The Grading of Recommendations, Assessment, Development and Evaluations framework will be used to assess the quality of the evidence contributing to each network assessment.Ethics and disseminationThis study does not require formal ethical approval because individual patient data will not be included. The findings will be disseminated through peer-reviewed publications or conference presentations.PROSPERO registration numberCRD42018085503.

scholarly journals Comparing Bayesian Posterior Passing with Meta-analysis

2020 ◽  
Author(s):  
Joshua Pritsker
Keyword(s):  
Publication Bias ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Future Research ◽  
Meta Analyses

Brand, von der Post, Ounsley, and Morgan (2019) introduced Bayesian posterior passing as an alternative to traditional meta-analyses. In this commentary I relate their procedure to traditional meta-analysis, showing that posterior passing is equivalent to fixed effects meta-analysis. To overcome the limitations of simple posterior passing, I introduce improved posterior passing methods to account for heterogeneity and publication bias. Additionally, practical limitations of posterior passing and the role that it can play in future research are discussed.

scholarly journals Global Burden of Anxiety and Depression among Cystic Fibrosis Patient: Systematic Review and Meta-Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mistire Teshome Guta ◽  
Tiwabwork Tekalign ◽  
Nefsu Awoke ◽  
Robera Olana Fite ◽  
Getahun Dendir ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
North America ◽  
Publication Bias ◽  
Meta Analysis ◽  
Systemic Review ◽  
Anxiety And Depression ◽  
Close Monitoring ◽  
The World ◽  
Trim And Fill ◽  
Meta Analyses

Aims. This systemic review and meta-analysis were aimed at determining the level of anxiety and depression among cystic fibrosis patients in the world. Methods. We conducted a systematic search of published studies from PubMed, EMBASE, MEDLINE, Cochrane, Scopus, Web of Science, CINAHL, and manually on Google Scholar. This meta-analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The quality of studies was assessed by the modified Newcastle-Ottawa Scale (NOS). Meta-analysis was carried out using a random-effects method using the STATA™ Version 14 software. Trim and fill analysis was done to correct the presence of significant publication bias. Result. From 419,820 obtained studies, 26 studies from 2 different parts of the world including 9766. The overall global pooled prevalence of anxiety and depression after correction for publication bias by trim and fill analysis was found to be 24.91(95% CI: 20.8-28.9) for anxiety. The subgroup analyses revealed with the lowest prevalence, 23.59%, (95% CI: 8.08, 39.09)) in North America and the highest, 26.77%, (95% CI: 22.5, 31.04) seen in Europe for anxiety and with the highest prevalence, 18.67%, (95% CI: 9.82, 27.5) in North America and the lowest, 13.27%, (95% CI: -10.05, 16.5) seen in Europe for depression. Conclusion. The global prevalence of anxiety and depression among cystic fibrosis patients is common. Therefore, close monitoring of the patient, regularly screening for anxiety and depression, and appropriate prevention techniques is recommended.

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