Institution
University of Bristol
Education•Bristol, United Kingdom•
About: University of Bristol is a education organization based out in Bristol, United Kingdom. It is known for research contribution in the topics: Population & Poison control. The organization has 44253 authors who have published 113186 publications receiving 4947181 citations. The organization is also known as: Bris..
Topics: Population, Poison control, Randomized controlled trial, Cohort study, Mendelian randomization
Papers published on a yearly basis
Papers
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TL;DR: Funnel plots, plots of the trials' effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials.
Abstract: Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Design: Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews . Main outcome measure: Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. Results: In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. Conclusions: A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution. Key messages Systematic reviews of randomised trials are the best strategy for appraising evidence; however, the findings of some meta-analyses were later contradicted by large trials Funnel plots, plots of the trials9 effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials Funnel plot asymmetry was found in 38% of meta-analyses published in leading general medicine journals and in 13% of reviews from the Cochrane Database of Systematic Reviews Critical examination of systematic reviews for publication and related biases should be considered a routine procedure
37,989 citations
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TL;DR: The Cochrane Collaboration’s tool for assessing risk of bias aims to make the process clearer and more accurate.
Abstract: Flaws in the design, conduct, analysis, and reporting of randomised trials can cause the effect of an intervention to be underestimated or overestimated. The Cochrane Collaboration’s tool for assessing risk of bias aims to make the process clearer and more accurate
22,227 citations
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11 Feb 1988TL;DR: In this paper, the gear predictor -corrector is used to calculate forces and torques in a non-equilibrium molecular dynamics simulation using Monte Carlo methods. But it is not suitable for the gear prediction problem.
Abstract: Introduction Statistical mechanics Molecular dynamics Monte Carlo methods Some tricks of the trade How to analyse the results Advanced simulation techniques Non-equilibrium molecular dynamics Brownian dynamics Quantum simulations Some applications Appendix A: Computers and computer simulation Appendix B: Reduced units Appendix C: Calculation of forces and torques Appendix D: Fourier transforms Appendix E: The gear predictor - corrector Appendix F: Programs on microfiche Appendix G: Random numbers References Index.
21,073 citations
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Monash University1, University of Amsterdam2, University of Paris3, Bond University4, University of Texas Health Science Center at San Antonio5, University of Ottawa6, American University of Beirut7, Oregon Health & Science University8, University of York9, Ottawa Hospital Research Institute10, University of Southern Denmark11, Johns Hopkins University12, Brigham and Women's Hospital13, Indiana University14, University of Bristol15, University College London16, University of Toronto17
TL;DR: The preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement as discussed by the authors was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found.
Abstract: The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
16,613 citations
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TL;DR: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative developed recommendations on what should be included in an accurate and complete report of an observational study, resulting in a checklist of 22 items (the STROBE statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.
Abstract: Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the Web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.
15,454 citations
Authors
Showing all 44997 results
Name | H-index | Papers | Citations |
---|---|---|---|
Walter C. Willett | 334 | 2399 | 413322 |
George Davey Smith | 224 | 2540 | 248373 |
Mika Kivimäki | 166 | 1515 | 141468 |
Gavin Davies | 159 | 2036 | 149835 |
George D. Yancopoulos | 158 | 496 | 93955 |
Pete Smith | 156 | 2464 | 138819 |
Marjo-Riitta Järvelin | 156 | 923 | 100939 |
Naveed Sattar | 155 | 1326 | 116368 |
Matthias Egger | 152 | 901 | 184176 |
Susan E. Hankinson | 151 | 789 | 88297 |
Debbie A Lawlor | 147 | 1114 | 101123 |
Shah Ebrahim | 146 | 733 | 96807 |
Christopher Hill | 144 | 1562 | 128098 |
Alan J. Silman | 141 | 708 | 92864 |
Barry Blumenfeld | 140 | 1909 | 105694 |