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.

Modifiable risk factors for prostate cancer mortality in London: forty years of follow-up in the Whitehall study

Abstract: The determinants of prostate cancer––aside from established but non-modifiable risk factors of increased age, black ethnicity, and a positive family history––are poorly understood. We examined the association of a series of baseline socioeconomic, behavioral, and metabolic characteristics with the risk of prostate cancer mortality in a 40-year follow-up of study members from the original Whitehall cohort study. During this period there were 578 prostate cancer deaths in 17,934 men. After adjustment for a series of baseline covariates, results from proportional hazards regression analyses indicated that marital status (hazard ratio; 95% confidence interval: widowed/divorced vs. married: 1.44; 0.95, 2.18), raised blood cholesterol (tertile 3 vs. 1: 1.35; 1.11, 1.65), and increased physical stature (tertile 3 vs. 1: 1.37; 1.09, 1.74) were associated with death from prostate cancer, although statistical significance at conventional levels was not apparent in all analyses. There was no evidence that physical activity, smoking habit, socio-economic status, component of either blood pressure or diabetes predicted the risk of death from this malignancy herein. In the present study, there was a suggestion that marital status, blood cholesterol, and height were risk indices for death from prostate cancer.

Inflation in epidemiology: “The proof and measurement of association between two things” revisited

Abstract: The details of methods of statistical analysis used in studies reported in the BMJ will often be skimmed rapidly by readers who want to quickly assimilate the main message. The exact nature of the statistical methods may become a focus of attention, but this can be seen as an arcane area, of interest perhaps to the specialist and pedant, but not to the general reader. Increasingly, however, the particular details of analytical methods can greatly influence the apparent nature and importance of the findings. This can be illustrated by reference to the recent paper and commentaries in the BMJ regarding new analyses of Intersalt data. One potentially contentious area is the manner in which the association between sodium excretion and blood pressure has been “corrected for regression dilution bias.” For many readers the basic principle of dealing with the underestimation of associations caused by poor measurement may seem reasonable, but the validity of applying the particular corrections which are used has to be taken on trust. Confusion may be increased by the presentation of a set of “updated” corrected estimates, to replace the already corrected estimates given in the initial Intersalt report.1 Comparison of the results reported in different ways reveals the degree to which such “corrections” can alter the picture. The difference in systolic blood pressure associated with 100 mmol higher 24 hour urinary sodium excretion is presented. The latter represents a considerable difference in sodium excretion—roughly two standard deviations in the British Intersalt centres or the difference between the means in the Kenyan and the British centres. In the original report the estimated blood pressure difference across this range was 1.6 mm Hg, which was reduced to 1.0 mm Hg on adjustment for body mass index, alcohol intake, and urinary potassium excretion. Applying an adjustment for regression …

Interaction of the solute niobium or molybdenum with grain boundaries in α-iron

Abstract: Three-dimensional atom probe (3DAP) was used to estimate solute–grain boundary interactions for Nb or Mo in α-Fe. By using the Langmuir–McLean isotherm, effective Gibbs free energies of segregation in α-Fe, ΔGb, at 800 °C were estimated to be −38±2 kJ mol−1 for Nb and −28±2 kJ mol−1 for Mo. The apparent width of solute segregation was shown to be broader when the direction of analysis is not perpendicular to the boundary plane, due to limits of the lateral resolution of the 3DAP. However, even taking this effect into account, we measured a width of solute segregation of 1.0–2.0 nm, i.e. extending a few atom layers on both side of grain boundary. In order to represent the actual solute segregation profile, a wedge-shaped interaction potential across grain boundaries was used for the estimation of the interaction from atom probe data. The difference in the solute–grain boundary interaction between Nb and Mo, and the solute distribution within grain boundary is also discussed.

Prevalence and functionality of paucimorphic and private MC4R mutations in a large, unselected European British population, scanned by meltMADGE.

Abstract: Identification of unknown mutations has remained laborious, expensive, and only viable for studies of selected cases. Population-based "reference ranges" of rarer sequence diversity are not available. However, the research and diagnostic interpretation of sequence variants depends on such information. Additionally, this is the only way to determine prevalence of severe, moderate, and silent mutations and is also relevant to the development of screening programs. We previously described a system, meltMADGE, suitable for mutation scanning at the population level. Here we describe its application to a population-based study of MC4R (melanocortin 4 receptor) mutations, which are associated with obesity. We developed nine assays representing MC4R and examined a population sample of 1,100 subjects. Two "paucimorphisms" were identified (c.307G>A/p.Val103Ile in 27 subjects and c.-178A>C in 22 subjects). Neither exhibited any anthropometric effects, whereas there would have been >90% power to detect a body mass index (BMI) effect of 0.5 kg/m(2) at P=0.01. Two "private" variants were also identified. c.335C>T/p.Thr112Met has been previously described and appears to be silent. A novel variant, c.260C>A/p.Ala87Asp, was observed in a subject with a BMI of 31.5 kg/m(2) (i.e., clinically obese) but not on direct assay of a further 3,525 subjects. This mutation was predicted to be deleterious and analysis using a cyclic AMP (cAMP) responsive luciferase reporter assay showed substantial loss of function of the mutant receptor. This population-based mutation scan of MC4R suggests that there is no severe MC4R mutation with high prevalence in the United Kingdom, but that obesity-causing MC4R mutation at 1 in 1,100 might represent one of the commonest autosomal dominant disorders in man.

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