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.

A framework for clinical evaluation of diagnostic technologies

Abstract: Most new diagnostic technologies have not been adequately assessed to determine whether their application improves health. Comprehensive evaluation of diagnostic technologies includes establishing technologic capability and determining the range of possible uses, diagnostic accuracy, impact on the health care provider, therapeutic impact and impact on patient outcome. Guidelines to determine whether each of these criteria have been met adequately are presented. Diagnostic technologies should be disseminated only if they are less expensive, produce fewer untoward effects and are at least as accurate as existing methods, if they eliminate the need for other investigations without loss of accuracy, or if they lead to institution of effective therapy. Establishing patient benefit often requires a randomized controlled trial in which patients receive the new test or an alternative diagnostic strategy. Other study designs are logistically less difficult but may not provide accurate assessment of benefit. Rigorous assessment of diagnostic technologies is needed for efficient use of health care resources.

Evaluating the Teaching of Evidence-Based Medicine

Abstract: AN INCREASING NUMBER OF MEDICAL SCHOOLS AND residency programs are instituting curricula for teaching the principles and practice of evidencebased medicine (EBM). For example, 95% of US internal medicine residency programs have journal clubs and 37% of US and Canadian internal medicine residencies have time dedicated for EBM. Curricula based on EBM are increasingly popular in residency programs in other specialties, including family medicine, pediatrics, obstetrics/ gynecology, and surgery. Despite the widespread teaching of EBM, however, most of what is known about the outcomes of evidence-based curricula relies on observational data. Although evaluation of the quality of research evidence is a core competency of EBM, the quantity and quality of the evidence for effectively teaching EBM are poor. Ironically, if one were to develop guidelines for how to teach EBM based on these results, they would be based on the lowest level of evidence. There are several reasons why the quality of the evidence for teaching EBM is so weak. Many of these problems are related to the limitations in educational research in general. First, quantitative research methods may be inadequate to capture the complexity of an educational system. Second, students and residents change frequently, making it difficult to retain a consistent sample. Third, the time allotted for a given intervention may be brief in the context of the overall medical curriculum. Fourth, educational institutions may be hesitant to pay students as research participants or to allocate them to unproved educational interventions. Fifth, because most educational interventions are unique to specific institutions, assessment of their effectiveness is usually limited by small sample sizes. Furthermore, even if such interventions could be instituted across multiple institutions, the problems of standardization and cointervention would be particularly challenging. Sixth, perhaps because they are simplest to measure, the most frequently reported outcomes are subjective variables such as satisfaction or self-reported changes in attitudes or knowledge, rather than more important assessments of objectively measured clinical skills or improved patient outcomes. Finally, granting agencies do not give priority to educational investigations, making it difficult to undertake definitive multicenter studies. Educators who have struggled to evaluate educational interventions will find these issues all too familiar. With the increasing prevalence of EBM teaching, however, highquality evidence is more important than ever. Assessment of EBM teaching has also presented some unique problems. For instance, we originally defined evidence-based practice in terms of 4 basic competencies: (1) recognition of a patient problem and construction of a structured clinical question; (2) ability to efficiently and effectively search the medical literature to retrieve the best available evidence to answer the clinical question; (3) critical appraisal of the evidence; and (4) integration of the evidence with all aspects of individual patient decision making to determine the best clinical care for the patient. Although these 4 skills were the most commonly reported curricular objectives in 99 internal medicine residencies that teach EBM, almost all the research on EBM education has focused exclusively on the third item: teaching critical appraisal skills. Examining this literature may yield useful insights into the difficulties of EBM educational research. Since critical appraisal skills involve the ability to differentiate strong from weak research methods, one might expect that this research would be of relatively high quality. In fact, most of these studies are methodologically weak. Using broad criteria to identify any reports of a graduate (residency) EBM curricula, Green identified 18 reports published between 1980 and 1997. Of these, 72% used a traditional journal club format to teach critical appraisal skills. Only 7 of the 18 studies evaluated the effectiveness of their intervention. Five of these 7 studies compared intervention with control (only 1 with randomized design); only 2 of 7 studies used any blinding. Of these 5 controlled studies, 2 used a validated outcome measure to evaluate critical appraisal skills. Measurement of behavioral change relied on self-report in all 5 studies, and none examined patient outcomes. Most reports did not evaluate their intervention

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.