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.

Meta-analysis: Beyond the grand mean?

Abstract: In the previous two articles1 2 we outlined the potentials and principles of meta-analysis and the practical steps in performing a meta-analysis. Now we will examine how to use meta-analysis to do more than simply combine the results from all the individual trials into a single effect estimate. Firstly, we discuss the advantages and disadvantages of performing subgroup analyses. Secondly, we consider the situation in which the differences in effects between individual trials are related in a graded way to an underlying phenomenon, such as the degree of mortality risk of the trial participants. #### Summary points Meta-analysis can be used to examine differences in treatment effects across trials; however, the fact that randomised trials are included in meta-analyses does not mean that comparisons between trials are also randomised comparisons Meta-analytic subgroup analyses, like subgroup analyses within trials, are prone to bias and need to be interpreted with caution A more reliable way of assessing differences in treatment effects is to relate outcome to some underlying patient characteristic on a continuous, or ordered, scale The underlying level of risk is a key variable which is often related to a given treatment effect, with patients at higher risk receiving more benefit then low risk patients Individual patient data, rather than published summary statistics, are often required for meaningful subgroup analyses The main aim of a meta-analysis is to produce an estimate of the average effect seen in trials of a particular treatment. The direction and magnitude of this average effect is intended to guide decisions about clinical practice for a wide range of patients. Clinicians are thus being asked to treat their patients as though each one is well represented by the patients in the clinical trials included in the meta-analysis. This runs against doctors' concerns to use the specific characteristics of a …

Maternal and Paternal Smoking During Pregnancy and Risk of ADHD Symptoms in Offspring: Testing for Intrauterine Effects

Abstract: Maternal smoking during pregnancy is associated with attention deficit hyperactivity disorder (ADHD) in offspring. It is assumed by many that this association is causal. Others suggest that observed associations are due to unmeasured genetic factors or other confounding factors. The authors compared risks of maternal smoking during pregnancy with those of paternal smoking during pregnancy. With a causal intrauterine effect, no independent association should be observed between paternal smoking and offspring ADHD. If the association is due to confounding factors, risks of offspring ADHD should be of similar magnitudes regardless of which parent smokes. This hypothesis was tested in 8,324 children from a well-characterized United Kingdom prospective cohort study, the Avon Longitudinal Study of Parents and Children (data from 1991–2000). Associations between offspring ADHD and maternal and paternal smoking during pregnancy were compared using regression analyses. Offspring ADHD symptoms were associated with exposure to both maternal and paternal smoking during pregnancy (mothers: β = 0.25, 95% confidence interval: 0.18, 0.32; fathers: β = 0.21, 95% confidence interval: 0.15, 0.27). When paternal smoking was examined in the absence of maternal smoking, associations remained and did not appear to be due to passive smoking exposure in utero. These findings suggest that associations between maternal smoking during pregnancy and child ADHD may be due to genetic or household-level confounding rather than to causal intrauterine effects.

Socio‐economic and dietary influences on leg length and trunk length in childhood: a reanalysis of the Carnegie (Boyd Orr) survey of diet and health in prewar Britain (1937–39)

Abstract: Social class differences in height have been recognised for many centuries. However, few studies have examined the extent to which these differences are made up of differences in leg length or trunk length. This paper reanalyses cross-sectional information on children examined in Britain in the 1930s. We assess associations between socio-economic status and diet and the components of childhood stature. The analyses were based on the records of 2990 children aged 2 years to 14 years 9 months who were examined in the Carnegie (Boyd Orr) survey of diet and health (1937-39). z-Scores for the measures of childhood stature were calculated using polynomial regression techniques with the study population as the standard. Univariable and multivariable statistical techniques were used to assess the relationships between childhood height, leg length and trunk length, and dietary and socio-economic factors measured at the level of the household. Leg length was the component of stature most strongly associated with measures of childhood diet and socio-economic status. A greater part of the difference in stature between socio-economic groups was caused by differences in leg length rather than trunk length. In multiple regression analyses, district of residence and family food expenditure were generally the two factors most strongly related to stature. In a subsample of the surveyed children, for whom birthweight information was available, trunk length and leg length were equally strongly related to birthweight. Leg length appears to be a particularly sensitive indicator of childhood socio-economic circumstances. Although contemporary studies highlight the importance of biological factors in determining childhood height, the data analysed in this study suggest that socio-economic circumstances were also important in explaining height differentials in prewar Britain.

Early life determinants of adult blood pressure.

Abstract: Purpose of reviewWhile treating high blood pressure in middle age is beneficial in terms of reducing the occurrence of cardiovascular disease, treated and well controlled hypertensive adults still have a substantial excess mortality and reduced survival compared with normotensives. Therefore, identi

The accuracy of the Framingham risk-score in different socioeconomic groups: a prospective study.

Abstract: Background The primary prevention of cardiovascular disease involves using the Framingham risk score to identify high risk patients and then prescribe preventive treatments. Aim To examine the performance of the Framingham risk score in different socioeconomic groups in a population with high rates of cardiovascular disease. Design of study A prospective study. Setting West of Scotland. Method The observed 10-year cardiovascular disease and coronary heart disease mortality rates in 5626 men and 6678 women free from cardiovascular disease from the Renfrew/Paisley Study were compared with predicted rates, stratified by socioeconomic class and by area deprivation score. Results The ratio of predicted to observed cardiovascular mortality rate in the 12 304 men and women with complete risk factor information was 0.56 (95% confidence interval [CI] = 0.52 to 0.60), a relative underestimation of 44%. Cardiovascular disease mortality was underestimated by 48% in manual participants (predicted over observed = 0.52, 95% CI = 0.48 to 0.56) compared to 31% in the non-manual participants (predicted over observed = 0.69, 95% CI = 0.60 to 0.81, P = 0.0005). Underestimation was also worse in participants from deprived areas ( P = 0.0017). Only 4.8% of individuals had a 10-year cardiovascular risk of >40% (equivalent to >30% 10-year coronary risk), and 81% of deaths occurred in the rest. If the Framingham score had been recalibrated for manual and non-manual members of this population, an additional 3611 individuals mainly from manual social classes would have reached the treatment threshold. Conclusion Currently recommended risk scoring methods underestimate risk in socioeconomically deprived individuals. The likely consequence is that preventive treatments are less available to the most needy.

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