George Davey Smith

Bio: George Davey Smith is an academic researcher from University of Bristol. The author has contributed to research in topics: Population & Mendelian randomization. The author has an hindex of 224, co-authored 2540 publications receiving 248373 citations. Previous affiliations of George Davey Smith include Keele University & Western Infirmary.

Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration

Abstract: Mendelian randomisation (MR) studies allow a better understanding of the causal effects of modifiable exposures on health outcomes, but the published evidence is often hampered by inadequate reporting. Reporting guidelines help authors effectively communicate all critical information about what was done and what was found. STROBE-MR (strengthening the reporting of observational studies in epidemiology using mendelian randomisation) assists authors in reporting their MR research clearly and transparently. Adopting STROBE-MR should help readers, reviewers, and journal editors evaluate the quality of published MR studies. This article explains the 20 items of the STROBE-MR checklist, along with their meaning and rationale, using terms defined in a glossary. Examples of transparent reporting are used for each item to illustrate best practices.

Exposures in childhood, adolescence and early adulthood and breast cancer risk: a systematic review of the literature

Abstract: A growing body of work indicates that exposures over the life course have important roles to play in the aetiology of breast cancer. This review synthesises the literature that has been published in the area of early life events and female breast cancer risk. The review finds some evidence, primarily from cohort studies on the relationship between birthweight and breast cancer, to suggest that in utero events are related to breast cancer risk in adulthood. Strong evidence to support a positive association between height and breast cancer exists. Postulated mechanisms for this relationship include the role of early diet in subsequent disease risk, and the influence of endogenous growth factors mediating the relationship. There is some evidence to suggest that leg length is the component of height which is generating the observed associations between height and breast cancer. There is no consistent pattern of association between relative weight in childhood or adolescence and risk of breast cancer. The evidence to suggest an association between physical activity in early life and breast cancer risk is convincing from case-control studies, but is not fully substantiated by the results of three cohort studies. There are inconsistent results regarding the association between smoking at a young age and breast cancer risk. There is little evidence for an association between passive smoking in early life and breast cancer risk. No clear association between early drinking and breast cancer risk exists. These results are discussed in relation to possible underlying mechanisms and health promotion strategies which could reduce breast cancer risk.

Confounding in epidemiological studies: why "independent" effects may not be all they seem.

Abstract: enrolling children because we ran out ofmoney. It was only at the very last moment, at this last special legislative session, through extraordinary efforts by some really committed folks in the governor's office, that a deal was struck to expand the programme so we could reopen enrolment. The programme provides subsidised health insurance to children who do not qualify for Medicaid for primary ambulatory care-not hospital care, no medications, no high-tech procedures, just basic routine care, getting the infection treated before it turns into a major problem. It's a very narrow, circumscribed programme. But we need to expand it to all eligible children. The legislators chose children because frankly, from a political standpoint, children are easiest to sell. But as we discussed, there's so much need for expanded access to programmes like this, even before we get to comprehensive reform. It can be effective, I think, in targeting the most needy populations and solving some of the biggest inequities in the system. So I think we can push ahead with things like insurance reform as we did in this legislative sessioncommunity rating, eliminating pre-existing conditions. We can push ahead with patchwork programmes like child health. Any amount of resources we can commit to that while we're trying to deal with the larger issues-how we're going to control costs in the major programmes and expand access and make sure that quality doesn't suffer at the same time. We have to do both. So I'd look forward to a lot of beneficial changes. I'd look forward to a more enlightened AIDS policy if we get a change of administration in Washington. I would hope even ifwe don't get a change ofadministration we would get a more enlightened AIDS policy, but I don't really see much prospect of that.

The tale wagged by the DAG: broadening the scope of causal inference and explanation for epidemiology.

Abstract: 'Causal inference', in 21st century epidemiology, has notably come to stand for a specific approach, one focused primarily on counterfactual and potential outcome reasoning and using particular representations, such as directed acyclic graphs (DAGs) and Bayesian causal nets. In this essay, we suggest that in epidemiology no one causal approach should drive the questions asked or delimit what counts as useful evidence. Robust causal inference instead comprises a complex narrative, created by scientists appraising, from diverse perspectives, different strands of evidence produced by myriad methods. DAGs can of course be useful, but should not alone wag the causal tale. To make our case, we first address key conceptual issues, after which we offer several concrete examples illustrating how the newly favoured methods, despite their strengths, can also: (i) limit who and what may be deemed a 'cause', thereby narrowing the scope of the field; and (ii) lead to erroneous causal inference, especially if key biological and social assumptions about parameters are poorly conceived, thereby potentially causing harm. As an alternative, we propose that the field of epidemiology consider judicious use of the broad and flexible framework of 'inference to the best explanation', an approach perhaps best developed by Peter Lipton, a philosopher of science who frequently employed epidemiologically relevant examples. This stance requires not only that we be open to being pluralists about both causation and evidence but also that we rise to the challenge of forging explanations that, in Lipton's words, aspire to 'scope, precision, mechanism, unification and simplicity'.

Use of genetic variation to separate the effects of early and later life adiposity on disease risk: mendelian randomisation study

Abstract: Objective To evaluate whether body size in early life has an independent effect on risk of disease in later life or whether its influence is mediated by body size in adulthood. Design Two sample univariable and multivariable mendelian randomisation. Setting The UK Biobank prospective cohort study and four large scale genome-wide association studies (GWAS) consortiums. Participants 453 169 participants enrolled in UK Biobank and a combined total of more than 700 000 people from different GWAS consortiums. Exposures Measured body mass index during adulthood (mean age 56.5) and self-reported perceived body size at age 10. Main outcome measures Coronary artery disease, type 2 diabetes, breast cancer, and prostate cancer. Results Having a larger genetically predicted body size in early life was associated with an increased odds of coronary artery disease (odds ratio 1.49 for each change in body size category unless stated otherwise, 95% confidence interval 1.33 to 1.68) and type 2 diabetes (2.32, 1.76 to 3.05) based on univariable mendelian randomisation analyses. However, little evidence was found of a direct effect (ie, not through adult body size) based on multivariable mendelian randomisation estimates (coronary artery disease: 1.02, 0.86 to 1.22; type 2 diabetes:1.16, 0.74 to 1.82). In the multivariable mendelian randomisation analysis of breast cancer risk, strong evidence was found of a protective direct effect for larger body size in early life (0.59, 0.50 to 0.71), with less evidence of a direct effect of adult body size on this outcome (1.08, 0.93 to 1.27). Including age at menarche as an additional exposure provided weak evidence of a total causal effect (univariable mendelian randomisation odds ratio 0.98, 95% confidence interval 0.91 to 1.06) but strong evidence of a direct causal effect, independent of early life and adult body size (multivariable mendelian randomisation odds ratio 0.90, 0.85 to 0.95). No strong evidence was found of a causal effect of either early or later life measures on prostate cancer (early life body size odds ratio 1.06, 95% confidence interval 0.81 to 1.40; adult body size 0.87, 0.70 to 1.08). Conclusions The findings suggest that the positive association between body size in childhood and risk of coronary artery disease and type 2 diabetes in adulthood can be attributed to individuals remaining large into later life. However, having a smaller body size during childhood might increase the risk of breast cancer regardless of body size in adulthood, with timing of puberty also putatively playing a role.

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

The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration

Abstract: Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users. Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions. The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials

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