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

Context: Altered vitamin D and calcium homeostasis may play a role in the development of type 2 diabetes mellitus (type 2 DM).Evidence Acquisition and Analyses: MEDLINE review was conducted through January 2007 for observational studies and clinical trials in adults with outcomes related to glucose homeostasis. When data were available to combine, meta-analyses were performed, and summary odds ratios (OR) are presented. is increasing at an alarming rate both nationally and worldwide, with more than 1 million new cases per year diagnosed in the United States alone (1). Diabetes is the fifth leading cause of death in the United States, and it is also a major cause of significant morbidity. Although our current methods of treating type 2 DM and its complications have improved, prevention of the disease is preferable. Indeed, epidemiological data suggest that nine of 10 cases of type 2 DM could be attributed to habits and forms of modifiable behavior (2). Potentially modifiable environmental risk factors for type 2 DM have been identified, the major one being obesity. Although weight loss (achieved by any means) has been shown to be successful in delaying type 2 DM, it is difficult to achieve and maintain long term. Therefore, identification of environmental and easily modified risk factors is urgently needed to prevent development of type 2 DM in the 41 million Americans who are at risk of the disease (3).The major and most well-known function of vitamin D is to maintain calcium and phosphorus homeostasis and promote bone mineralization. However, recent evidence suggests that vitamin D and calcium homeostasis may also be important for a variety of nonskeletal outcomes including neuromuscular function and falls, psoriasis, multiple sclerosis, and colorectal and prostate cancer (4, 5). Based on basic and animal studies, vitamin D and calcium have also been suspected as modifiers of diabetes risk. Vitamin D insufficiency has long been suspected as a risk factor for type 1 diabetes based on animal and human observational studies (6). More recently, there is accumulating evidence to suggest that altered vitamin D and calcium homeostasis may also play a role in the development of type 2 DM. The purpose of our systematic review was to examine: 1) the association between vitamin D and calcium status and risk of type 2 DM; and 2) the effect of vitamin D and/or calcium supplementation on glucose metabolism. Materials and MethodsWe searched MEDLINE for English-language literature through January 2007 for observational studies on the association between vitamin D/calcium status (defined by serum 25-hydroxyvitamin D (25-OHD) concentration, and vitamin D, calcium, or dairy intake) and type 2 DM (prevalence or incidence) and for randomized controlled trials of the effect of vitamin D and/or calcium supplementation in nonpregnant adults on outcomes related to glucose homeostasis. We also examined metabolic syndrome (prevalence or incidence) as an outcome of interest, given that insulin resistance, a feature of type 2 DM, is consid...

The final common pathway for most systematic reviews is a statistical summary of the data, or meta-analysis. The complex methods used in meta-analyses should always be complemented by clinical acumen and common sense in designing the protocol of a systematic review, deciding which data can be combined, and determining whether data should be combined. Both continuous and binary data can be pooled. Most meta-analyses summarize data from randomized trials, but other applications, such as the evaluation of diagnostic test performance and observational studies, have also been developed. The statistical methods of meta-analysis aim at evaluating the diversity (heterogeneity) among the results of different studies, exploring and explaining observed heterogeneity, and estimating a common pooled effect with increased precision. Fixed-effects models assume that an intervention has a single true effect, whereas random-effects models assume that an effect may vary across studies. Meta-regression analyses, by using each study rather than each patient as a unit of observation, can help to evaluate the effect of individual variables on the magnitude of an observed effect and thus may sometimes explain why study results differ. It is also important to assess the robustness of conclusions through sensitivity analyses and a formal evaluation of potential sources of bias, including publication bias and the effect of the quality of the studies on the observed effect.

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.

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