Funnel plots, and tests for funnel plot asymmetry, have been widely used to examine bias in the results of meta-analyses. Funnel plot asymmetry should not be equated with publication bias, because it has a number of other possible causes. This article describes how to interpret funnel plot asymmetry, recommends appropriate tests, and explains the implications for choice of meta-analysis model This article recommends how to examine and interpret funnel plot asymmetry (also known as small study effects 2 ) in meta-analyses of randomised controlled trials. The recommendations are based on a detailed MEDLINE review of literature published up to 2007 and discussions among methodologists, who extended and adapted guidance previously summarised in the Cochrane Handbook for Systematic Reviews of Interventions. 7 What is a funnel plot?A funnel plot is a scatter plot of the effect estimates from individual studies against some measure of each study's size or precision. The standard error of the effect estimate is often chosen as the measure of study size and plotted on the vertical axis 8 with a reversed scale that places the larger, most powerful studies towards the top. The effect estimates from smaller studies should scatter more widely at the bottom, with the spread narrowing among larger studies. 9 In the absence of bias and between study heterogeneity, the scatter will be due to sampling variation alone and the plot will resemble a symmetrical inverted funnel (fig 1). A triangle centred on a fixed effect summary estimate and extending 1.96 standard errors either side willCorrespondence to: J A C Sterne jonathan.sterne@bristol.ac.ukTechnical appendix (see

The PRISMA statement is a reporting guideline designed to improve the completeness of reporting of systematic reviews and meta-analyses. Authors have used this guideline worldwide to prepare their reviews for publication. In the past, these reports typically compared 2 treatment alternatives. With the evolution of systematic reviews that compare multiple treatments, some of them only indirectly, authors face novel challenges for conducting and reporting their reviews. This extension of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement was developed specifically to improve the reporting of systematic reviews incorporating network meta-analyses. A group of experts participated in a systematic review, Delphi survey, and face-to-face discussion and consensus meeting to establish new checklist items for this extension statement. Current PRISMA items were also clarified. A modified, 32-item PRISMA extension checklist was developed to address what the group considered to be immediately relevant to the reporting of network meta-analyses. This document presents the extension and provides examples of good reporting, as well as elaborations regarding the rationale for new checklist items and the modification of previously existing items from the PRISMA statement. It also highlights educational information related to key considerations in the practice of network meta-analysis. The target audience includes authors and readers of network meta-analyses, as well as journal editors and peer reviewers.

BACKGROUND Estimates of glomerular filtration rate (GFR) that are based on serum creatinine are routinely used; however, they are imprecise, potentially leading to the overdiagnosis of chronic kidney disease. Cystatin C is an alternative filtration marker for estimating GFR. METHODS Using cross-sectional analyses, we developed estimating equations based on cystatin C alone and in combination with creatinine in diverse populations totaling 5352 participants from 13 studies. These equations were then validated in 1119 participants from 5 different studies in which GFR had been measured. Cystatin and creatinine assays were traceable to primary reference materials. RESULTS Mean measured GFRs were 68 and 70 ml per minute per 1.73 m2 of body-surface area in the development and validation data sets, respectively. In the validation data set, the creatinine–cystatin C equation performed better than equations that used creatinine or cystatin C alone. Bias was similar among the three equations, with a median difference between measured and estimated GFR of 3.9 ml per minute per 1.73 m2 with the combined equation, as compared with 3.7 and 3.4 ml per minute per 1.73 m2 with the creatinine equation and the cystatin C equation (P = 0.07 and P = 0.05), respectively. Precision was improved with the combined equation (inter-quartile range of the difference, 13.4 vs. 15.4 and 16.4 ml per minute per 1.73 m2, respectively [P = 0.001 and P<0.001]), and the results were more accurate (percentage of estimates that were >30% of measured GFR, 8.5 vs. 12.8 and 14.1, respectively [P<0.001 for both comparisons]). In participants whose estimated GFR based on creatinine was 45 to 74 ml per minute per 1.73 m2, the combined equation improved the classification of measured GFR as either less than 60 ml per minute per 1.73 m2 or greater than or equal to 60 ml per minute per 1.73 m2 (net reclassification index, 19.4% [P<0.001]) and correctly reclassified 16.9% of those with an estimated GFR of 45 to 59 ml per minute per 1.73 m2 as having a GFR of 60 ml or higher per minute per 1.73 m2. CONCLUSIONS The combined creatinine–cystatin C equation performed better than equations based on either of these markers alone and may be useful as a confirmatory test for chronic kidney disease. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases.)

OBJECTIVE -To evaluate the efficacy of self-management education on GHb in adults with type 2 diabetes. Medline (1980), Cinahl (1982, and the Educational Resources Information Center database (ERIC) (1980 -1999), and we manually searched review articles, journals with highest topic relevance, and reference lists of included articles. Studies were included if they were randomized controlled trials that were published in the English language, tested the effect of self-management education on adults with type 2 diabetes, and reported extractable data on the effect of treatment on GHb. A total of 31 studies of 463 initially identified articles met selection criteria. We computed net change in GHb, stratified by follow-up interval, tested for trial heterogeneity, and calculated pooled effects sizes using random effects models. We examined the effect of baseline GHb, follow-up interval, and intervention characteristics on GHb. RESEARCH DESIGN AND METHODS -We searched for English language trials inRESULTS -On average, the intervention decreased GHb by 0.76% (95% CI 0.34 -1.18) more than the control group at immediate follow-up; by 0.26% (0.21% increase -0.73% decrease) at 1-3 months of follow-up; and by 0.26% (0.05-0.48) at Ն4 months of follow-up. GHb decreased more with additional contact time between participant and educator; a decrease of 1% was noted for every additional 23.6 h (13.3-105.4) of contact.CONCLUSIONS -Self-management education improves GHb levels at immediate followup, and increased contact time increases the effect. The benefit declines 1-3 months after the intervention ceases, however, suggesting that learned behaviors change over time. Further research is needed to develop interventions effective in maintaining long-term glycemic control.

Serum cystatin C level alone provides GFR estimates that are nearly as accurate as serum creatinine level adjusted for age, sex, and race, thus providing an alternative GFR estimate that is not linked to muscle mass. An equation including serum cystatin C level in combination with serum creatinine level, age, sex, and race provides the most accurate estimates.

OBJECTIVE -Numerous studies in the U.S. and elsewhere have reported an increased risk of gestational diabetes mellitus (GDM) among women who are overweight or obese compared with lean or normal-weight women. Despite the number and overall consistency of studies reporting a higher risk of GDM with increasing weight or BMI, the magnitude of the association remains uncertain. This meta-analysis was conducted to better estimate this risk and to explore differences across studies. We used a Bayesian model to perform the meta-analysis and meta-regression. We included cohort-designed studies that reported obesity measures reflecting pregnancy body mass, that had a normal-weight comparison group, and that presented data allowing a quantitative measurement of risk. RESEARCH DESIGN AND METHODSRESULTS -Twenty studies were included in the meta-analysis. The unadjusted ORs of developing GDM were 2.14 (95% CI 1.82-2.53), 3.56 (3.05-4.21), and 8.56 (5.07-16.04) among overweight, obese, and severely obese compared with normal-weight pregnant women, respectively. The meta-regression analysis found no evidence that these estimates were affected by selected study characteristics (publication date, study location, parity, type of data collection [retrospective vs. prospective], and prevalence of GDM among normal-weight women).CONCLUSIONS -Our findings indicate that high maternal weight is associated with a substantially higher risk of GDM.

It is known that the existence of publication bias can influence the conclusions of a meta-analysis. Some methods have been developed to deal with publication bias, but issues remain. One particular method called 'trim and fill' is designed to adjust for publication bias. The method, which is intuitively appealing and comprehensible by non-statisticians, is based on a simple and popular graphical tool called the funnel plot. We present a simulation study designed to evaluate the behaviour of this method. Our results indicate that when the studies are heterogeneous (that is, when they estimate different effects), trim and fill may inappropriately adjust for publication bias where none exists. We found that trim and fill may spuriously adjust for non-existent bias if (i) the variability among studies causes some precisely estimated studies to have effects far from the global mean or (ii) an inverse relationship between treatment efficacy and sample size is introduced by the studies' a priori power calculations. The results suggest that the funnel plot itself is inappropriate for heterogeneous meta-analyses. Selection modelling is an alternative method warranting further study. It performed better than trim and fill in our simulations, although its frequency of convergence varied, depending on the simulation parameters.

Cystatin C is gaining acceptance as an endogenous filtration marker. Factors other than glomerular filtration rate (GFR) that affect the serum level have not been carefully studied. In a cross-sectional analysis of a pooled dataset of participants from clinical trials and a clinical population with chronic kidney disease (N=3418), we related serum levels of cystatin C and creatinine to clinical and biochemical variables after adjustment for GFR using errors-in-variables models to account for GFR measurement error. GFR was measured as urinary clearance of 125I-iothalamate and 15Cr-EDTA. Cystatin C was assayed at a single laboratory and creatinine was standardized to reference methods. Mean (SD) creatinine and cystatin C were 2.1 (1.1) mg/dL and 1.8 (0.8) mg/L, respectively. After adjustment for GFR, cystatin C was 4.3% lower for every 20 years of age, 9.2% lower for female sex but only 1.9% lower in blacks. Diabetes was associated with 8.5% higher levels of cystatin C and 3.9% lower levels of creatinine. Higher C-reactive protein and white blood cell count and lower serum albumin were associated with higher levels of cystatin C and lower levels of creatinine. Adjustment for age, sex and race had a greater effect on association of factors with creatinine than cystatin C. In conclusion, cystatin C is affected by factors other than GFR. Clinicians should consider these factors when interpreting the serum levels or GFR estimates from cystatin C.