Gordon H. Guyatt

Bio: Gordon H. Guyatt is an academic researcher from McMaster University. The author has contributed to research in topics: Randomized controlled trial & Evidence-based medicine. The author has an hindex of 231, co-authored 1620 publications receiving 228631 citations. Previous affiliations of Gordon H. Guyatt include Memorial Sloan Kettering Cancer Center & Cayetano Heredia University.

Reporting, handling and assessing the risk of bias associated with missing participant data in systematic reviews: a methodological survey

Abstract: Objectives: To describe how systematic reviewers are reporting missing data for dichotomous outcomes, handling them in the analysis and assessing the risk of associated bias. Methods: We searched MEDLINE and the Cochrane Database of Systematic Reviews for systematic reviews of randomised trials published in 2010, and reporting a meta-analysis of a dichotomous outcome. We randomly selected 98 Cochrane and 104 non-Cochrane systematic reviews. Teams of 2 reviewers selected eligible studies and abstracted data independently and in duplicate using standardised, piloted forms with accompanying instructions. We conducted regression analyses to explore factors associated with using complete case analysis and with judging the risk of bias associated with missing participant data. Results: Of Cochrane and non-Cochrane reviews, 47% and 7% (p<0.0001), respectively, reported on the number of participants with missing data, and 41% and 9% reported a plan for handling missing categorical data. The 2 most reported approaches for handling missing data were complete case analysis (8.5%, out of the 202 reviews) and assuming no participants with missing data had the event (4%). The use of complete case analysis was associated only with Cochrane reviews (relative to non-Cochrane: OR=7.25; 95% CI 1.58 to 33.3, p=0.01). 65% of reviews assessed risk of bias associated with missing data; this was associated with Cochrane reviews (relative to nonCochrane: OR=6.63; 95% CI 2.50 to 17.57, p=0.0001), and the use of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology (OR=5.02; 95% CI 1.02 to 24.75, p=0.047).

Allergy, and Immunology Physicians/American College of Asthma, American College of Chest Therapy: Evidence-Based Guidelines: Device Selection and Outcomes of Aerosol

Abstract: 2005;127;335-371 Chest Rajiv Dhand, Joseph L. Rau, Gerald C. Smaldone and Gordon Guyatt Myrna B. Dolovich, Richard C. Ahrens, Dean R. Hess, Paula Anderson, Allergy, and Immunology Physicians/American College of Asthma, American College of Chest : * Therapy: Evidence-Based Guidelines Device Selection and Outcomes of Aerosol http://chestjournal.chestpubs.org/content/127/1/335.full.html services can be found online on the World Wide Web at: The online version of this article, along with updated information and C1.html http://chestjournal.chestpubs.org/content/suppl/2005/01/19/127.1.335.D Supplemental material related to this article is available at: ISSN:0012-3692 ) http://chestjournal.chestpubs.org/site/misc/reprints.xhtml ( written permission of the copyright holder. this article or PDF may be reproduced or distributed without the prior Dundee Road, Northbrook, IL 60062. All rights reserved. No part of Copyright2005by the American College of Chest Physicians, 3300 Physicians. It has been published monthly since 1935. is the official journal of the American College of Chest Chest

PCSK9 inhibitors and ezetimibe with or without statin therapy for cardiovascular risk reduction: a systematic review and network meta-analysis

Abstract: Abstract Objective To compare the impact of ezetimibe and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors on cardiovascular outcomes in adults taking maximally tolerated statin therapy or who are statin intolerant. Design Network meta-analysis. Data sources Medline, EMBASE, and Cochrane Library up to 31 December 2020. Eligibility criteria for selecting studies Randomised controlled trials of ezetimibe and PCSK9 inhibitors with ≥500 patients and follow-up of ≥6 months. Main outcome measures We performed frequentist fixed-effects network meta-analysis and GRADE (grading of recommendations, assessment, development, and evaluation) to assess certainty of evidence. Results included relative risks (RR) and absolute risks per 1000 patients treated for five years for non-fatal myocardial infarction (MI), non-fatal stroke, all-cause mortality, and cardiovascular mortality. We estimated absolute risk differences assuming constant RR (estimated from network meta-analysis) across different baseline therapies and cardiovascular risk thresholds; the PREDICT risk calculator estimated cardiovascular risk in primary and secondary prevention. Patients were categorised at low to very high cardiovascular risk. A guideline panel and systematic review authors established the minimal important differences (MID) of 12 per 1000 for MI and 10 per 1000 for stroke. Results We identified 14 trials assessing ezetimibe and PCSK9 inhibitors among 83 660 adults using statins. Adding ezetimibe to statins reduced MI (RR 0.87 (95% confidence interval 0.80 to 0.94)) and stroke (RR 0.82 (0.71 to 0.96)) but not all-cause mortality (RR 0.99 (0.92 to 1.06)) or cardiovascular mortality (RR 0.97 (0.87 to 1.09)). Similarly, adding PCSK9 inhibitor to statins reduced MI (0.81 (0.76 to 0.87)) and stroke (0.74 (0.64 to 0.85)) but not all-cause (0.95 (0.87 to 1.03)) or cardiovascular mortality (0.95 (0.87 to 1.03)). Among adults with very high cardiovascular risk, adding PCSK9 inhibitor was likely to reduce MI (16 per 1000) and stroke (21 per 1000) (moderate to high certainty); whereas adding ezetimibe was likely to reduce stroke (14 per 1000), but the reduction of MI (11 per 1000) (moderate certainty) did not reach MID. Adding ezetimibe to PCSK9 inhibitor and statin may reduce stroke (11 per 1000), but the reduction of MI (9 per 1000) (low certainty) did not reach MID. Adding PCSK9 inhibitors to statins and ezetimibe may reduce MI (14 per 1000) and stroke (17 per 1000) (low certainty). Among adults with high cardiovascular risk, adding PCSK9 inhibitor probably reduced MI (12 per 1000) and stroke (16 per 1000) (moderate certainty); adding ezetimibe probably reduced stroke (11 per 1000), but the reduction in MI did not achieve MID (8 per 1000) (moderate certainty). Adding ezetimibe to PCSK9 inhibitor and statins did not reduce outcomes beyond MID, while adding PCSK9 inhibitor to ezetimibe and statins may reduce stroke (13 per 1000). These effects were consistent in statin-intolerant patients. Among moderate and low cardiovascular risk groups, adding PCSK9 inhibitor or ezetimibe to statins yielded little or no benefit for MI and stroke. Conclusions Ezetimibe or PCSK9 inhibitors may reduce non-fatal MI and stroke in adults at very high or high cardiovascular risk who are receiving maximally tolerated statin therapy or are statin-intolerant, but not in those with moderate and low cardiovascular risk.

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

Abstract: David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.

Abstract: Systematic reviews and meta-analyses have become increasingly important in health care. Clinicians read them to keep up to date with their field,1,2 and they are often used as a starting point for developing clinical practice guidelines. Granting agencies may require a systematic review to ensure there is justification for further research,3 and some health care journals are moving in this direction.4 As with all research, the value of a systematic review depends on what was done, what was found, and the clarity of reporting. As with other publications, the reporting quality of systematic reviews varies, limiting readers' ability to assess the strengths and weaknesses of those reviews. Several early studies evaluated the quality of review reports. In 1987, Mulrow examined 50 review articles published in 4 leading medical journals in 1985 and 1986 and found that none met all 8 explicit scientific criteria, such as a quality assessment of included studies.5 In 1987, Sacks and colleagues6 evaluated the adequacy of reporting of 83 meta-analyses on 23 characteristics in 6 domains. Reporting was generally poor; between 1 and 14 characteristics were adequately reported (mean = 7.7; standard deviation = 2.7). A 1996 update of this study found little improvement.7 In 1996, to address the suboptimal reporting of meta-analyses, an international group developed a guidance called the QUOROM Statement (QUality Of Reporting Of Meta-analyses), which focused on the reporting of meta-analyses of randomized controlled trials.8 In this article, we summarize a revision of these guidelines, renamed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses), which have been updated to address several conceptual and practical advances in the science of systematic reviews (Box 1). Box 1 Conceptual issues in the evolution from QUOROM to PRISMA

Measuring inconsistency in meta-analyses

Abstract: Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis. Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4 Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted? A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …

Bias in meta-analysis detected by a simple, graphical test

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

Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.

Abstract: This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.