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.

A putative functional polymorphism in the IGF-I gene: association studies with type 2 diabetes, adult height, glucose tolerance, and fetal growth in U.K. populations.

Abstract: IGF-I has a critical role in growth and metabolism. A microsatellite polymorphism 1 kb upstream to the IGF-I gene has recently been associated with several adult phenotypes. In a large Dutch cohort, the absence of the commonest allele (Z) was associated with reduced serum IGF-I levels, reduced height, and an increased risk of type 2 diabetes and myocardial infarction. This result has not been replicated, and the role of this polymorphism in these traits in U.K. subjects is not known. We sought further evidence for the involvement of this variant in type 2 diabetes using a case-control study and IGF-I and diabetes-related traits in a population cohort of 640 U.K. individuals aged 25 years. Absence of the common allele was not associated with type 2 diabetes (odds ratio 0.70, 95% CI 0.47-1.04 for X/X versus Z/Z genotype, chi(2) test for trend across genotypes, P = 0.018). In the population cohort, the common allele (Z) was associated with decreased IGF-I levels (P = 0.01), contrary to the Dutch study, but not with adult height (P = 0.23), glucose tolerance (P = 0.84), oral glucose tolerance test-derived values of beta-cell function (P = 0.90), or insulin resistance (P = 0.66). There was no association with measures of fetal growth, including birth weight (P = 0.17). Our results do not support the previous associations and suggest that the promoter microsatellite is unlikely to be functionally important.

A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals

Abstract: Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of long-term mortality in the circulation of 44,168 individuals (age at baseline 18–109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex (C-statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality (C-statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation.

Socioeconomic Position in Childhood and Early Adult Life and Risk of Mortality: A Prospective Study of the Mothers of the 1958 British Birth Cohort

Abstract: Objectives. We sought to establish whether women’s childhood socioeconomic position influenced their risk of mortality separately from the effects of adult socioeconomic position. Methods. We examined 11855 British women aged 14 to 49 years, with mortality follow-up over a 45-year period. Results. Trends according to childhood social class were observed for all-cause mortality, circulatory disease, coronary heart disease, respiratory disease, chronic obstructive pulmonary disease, stroke, lung cancer, and stomach cancer, with higher death rates among members of unskilled manual groups. Associations attenuated after adjustment for adult social class, smoking, and body mass index. No trend was seen for breast cancer or accidents and violence. Adverse social conditions in both childhood and adulthood were associated with higher death rates from coronary heart disease and respiratory disease. Stomach cancer was influenced primarily by childhood conditions and lung cancer by factors in adult life. Conclusions....

Where now for meta-analysis?

Abstract: In the short time since its introduction, meta-analysis, the statistical pooling of the results from independent but ‘combinable’ studies, has established itself as an influential branch of clinical epidemiology and health services research, with hundreds of meta-analyses published in the medical literature each year. 1 This issue of the International Journal of Epidemiology contains several papers 2‐9 that address methodological issues in metaanalytic research, a review article on where we stand with systematic reviews in observational epidemiology 10 and three meta-analyses of observational studies. 11‐13 Publication of a themed issue on meta-analysis by an epidemiological journal begs several questions: Where does meta-analysis come from? Does it deserve the attention it is currently getting? And where should it be going next? The statistical basis of meta-analysis reaches back to the 17th century when, in astronomy, intuition and experience suggested that combinations of data might be better than attempts

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