How do you interpret a funnel plot in a meta-analysis?

How do you interpret a funnel plot in a meta-analysis?

In a funnel plot, the weight of each study, the sample size, or the inverse of the variance is plotted against the size of its treatment effect in a meta-analysis. This plot should be shaped like an inverted funnel if there is no publication bias; asymmetric funnel plots may suggest publication bias.

What does a funnel plot indicate?

A funnel plot is a scatterplot of treatment effect against a measure of study precision. It is used primarily as a visual aid for detecting bias or systematic heterogeneity. A symmetric inverted funnel shape arises from a ‘well-behaved’ data set, in which publication bias is unlikely.

What does a good funnel plot look like?

The plot should ideally resemble a pyramid or inverted funnel, with scatter due to sampling variation. The shape is expected because the studies have a wide range of standard errors. If the standard errors were the same size, the studies would all fall on a horizontal line.

What tests are used to examine funnel plot asymmetry?

Egger’s test is commonly used to assess potential publication bias in a meta-analysis via funnel plot asymmetry (Egger’s test is a linear regression of the intervention effect estimates on their standard errors weighted by their inverse variance).

What does a high i2 mean?

The I^2 indicates the level of of heterogeneity. It can take values from 0% to 100%. If I^2 ≤ 50%, studies are considered homogeneous, and a fixed effect model of meta-analysis can be used. If I^2 > 50%, the heterogeneity is high, and one should usea random effect model for meta-analysis.

What is trim and fill?

The idea of the trim-and-fill method is to first trim the studies that cause a funnel plot’s asymmetry so that the overall effect estimate produced by the remaining studies can be considered minimally impacted by publication bias, and then to fill imputed missing studies in the funnel plot based on the bias-corrected …

What is heterogeneity in meta-analysis?

Heterogeneity in meta-analysis refers to the variation in study outcomes between studies. StatsDirect calls statistics for measuring heterogentiy in meta-analysis ‘non-combinability’ statistics in order to help the user to interpret the results. Measuring the inconsistency of studies’ results.

How many studies is a funnel plot?

As a rule of thumb, tests for funnel plot asymmetry should be used only when there are at least 10 studies included in the meta-analysis, because when there are fewer studies the power of the tests is too low to distinguish chance from real asymmetry.

How many studies are required for a funnel plot?

How much heterogeneity is acceptable in meta-analysis?

0% to 40%: might not be important; 30% to 60%: may represent moderate heterogeneity*; 50% to 90%: may represent substantial heterogeneity*;

Is high heterogeneity good or bad?

Having statistical heterogeneity is not a good or bad thing in and of itself for the analysis; however, it’s useful to know to design, choose and interpret statisti- cal analyses. Indeed, the comparison of heterogeneity often will be the outcome of interest, especially in quality fields.

How many studies are needed for a funnel plot?

10 studies
As a rule of thumb, tests for funnel plot asymmetry should be used only when there are at least 10 studies included in the meta-analysis, because when there are fewer studies the power of the tests is too low to distinguish chance from real asymmetry.

What is Duval and Tweedie trim and fill method?

Page 1. A Nonparametric “Trim and Fill” Method of. Accounting for Publication Bias in Meta-Analysis. Sue DUVAL and Richard TWEEDIE. Meta-analysis collects and synthesizes results from individual studies to estimate an overall effect size.

How do you check for publication bias?

The main graphical method for identifying publication bias is the use of funnel plots. A funnel plot is a plot of effect size against sample size or some other indicator of the precision of the estimate. To illustrate funnel plots I have used simulated data, where we know that there is no publication bias.

What is a funnel plot systematic review?

A funnel plot is a simple scatter plot of the intervention effect estimates from individual studies against some measure of each study’s size or precision. In common with forest plots, it is most common to plot the effect estimates on the horizontal scale, and thus the measure of study size on the vertical axis.

How do you read trim and fill?

How do you interpret meta-analysis results?

To interpret a meta-analysis, the reader needs to understand several concepts, including effect size, heterogeneity, the model used to conduct the meta-analysis, and the forest plot, a graphical representation of the meta-analysis.

How do you interpret a heterogeneity meta-analysis?

A rough guide to interpretation is as follows:

  1. 0% to 40%: might not be important;
  2. 30% to 60%: may represent moderate heterogeneity*;
  3. 50% to 90%: may represent substantial heterogeneity*;
  4. 75% to 100%: considerable heterogeneity*.

Should funnel plots be used in meta-analyses?

The appendix describes the proposed tests, explains the reasons that some were not recommended, and discusses funnel plots for intervention effects measured as risk ratios, risk differences, and standardised mean differences. Our recommendations imply that tests for funnel plot asymmetry should be used in only a minority of meta-analyses. 29

What is the role of funnel plots in the workup of publication bias?

Peters J, Sutton AJ, Jones DR, Abrams KR, Rushton L. Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry. J Clin Epidemiol 2008;61:991-6. Harbord RM, Egger M, Sterne JA.

Is there a test for funnel plot asymmetry?

The 1997 paper describing the test for funnel plot asymmetry proposed by Egger et al 1 is one of the most cited articles in the history of BMJ. 1 Despite the recommendations contained in this and subsequent papers, 2 3 funnel plot asymmetry is often, wrongly, equated with publication or other reporting biases.

What is the purpose of the funnel plot and Egger’s test?

The purpose of the funnel plot and Egger’s test was to detect possible bias in the trials that were identified and included in the meta-analysis ( a is true) Failure to include in a meta-analysis all of the relevant studies that have been conducted is often, wrongly, attributed solely to publication bias.