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Published in Ken Addley, MCQs, MEQs and OSPEs in Occupational Medicine, 2023
The methods for the review, including how the quality of studies is assessed, should be agreed before the study commences, and should be published in the methods. The diamond represents the pooled results of the included studies. Clinical heterogeneity reflects varied methods such as selection of patients and range of interventions. Statistical heterogeneity reflects odds ratios amongst the studies—some indicate harm from the intervention, some benefit. It can be assessed using a Chi-squared test. A funnel plot is a graphical representation that illustrates whether there may be publication bias when selecting the studies for the analysis. A search strategy should include more than one database and include an adequate range of keywords and not just one language.
Medications
Published in Henry J. Woodford, Essential Geriatrics, 2022
Funnel plots are used to evaluate potential reporting bias (including publication and other forms of bias, such as language bias), which, when present, creates plot asymmetry. However, other possible causes of asymmetry include varying methodological quality of the studies included (i.e. heterogeneity). A funnel plot is a scatter plot of the estimated effect size of individual studies (the X-axis, i.e. the outcome of interest) against a measure of study precision or size (the Y-axis, often 1/SE) (seeFigure 3.2).22 Studies with larger sample sizes tend to give more precise results and have a smaller SE (i.e. placed nearer the top). A wider scatter is expected with smaller studies towards the bottom. A triangle representing 1.96 × SE on each side of the fixed effect summary estimate is often superimposed. A symmetrical pattern with a funnel shape is expected if all relevant studies are included. Asymmetry suggests bias towards the reporting of positive studies, which leads to a tendency for meta-analyses to over-estimate treatment effect size. However, they can be misleading if only a few trials have been performed (e.g. less than ten). Asymmetry can also be caused by study heterogeneity, i.e. lower quality studies can report larger treatment effect sizes.22
Publication Bias in Meta-Analysis
Published in Ding-Geng (Din) Chen, Karl E. Peace, Applied Meta-Analysis with R and Stata, 2021
Finally, as discussed above, there are possible reasons for funnel plot asymmetry other than publication bias. Therefore, the funnel plot asymmetry tests discussed so far should not be viewed as tests for detecting publication bias, but rather as tests for “small-study effects” in a meta-analysis (see, Rothstein et al., 2005).
Effects of Positive Psychotherapy for People with Psychosis: A Systematic Review and Meta-Analysis
Published in Issues in Mental Health Nursing, 2023
Heeseung Choi, Soyoun Shin, Gumhee Lee
A funnel plot is a graphical approach used to check for indications of publication bias (Figure 3). If the funnel plot is symmetrical, it indicates no publication bias (Higgins et al., 2019). The trim-and-fill method was used to verify publication bias when the research results were skewed to one side or omitted from the mean (Higgins et al., 2003). As a result of correcting the study assumed to be omitted by the trim-and-fill method, 12 effect sizes for well-being were filled in the left direction, and the adjusted effect size was 0.48, which was reduced from the previous effect size of 0.99. However, the adjusted effect size was still statistically significant (95% CI = 0.11 to 0.85, p = 0.010). Hence, it was not interpreted as an error that could affect the study results. As for psychiatric symptoms, there was no additional filled study. Therefore, the effect size was the same as −1.10, and the results were statistically significant (95% CI= −1.97 to −0.23, p = 0.014).
Estimates of the prevalence of occult HBV infection in Asia: a systematic review and meta-analysis
Published in Infectious Diseases, 2022
Wen Yangyang Xie, Changfeng Sun, Hongyan He, Cunliang Deng, Yunjian Sheng
The prevalence of OBI was calculated for each study and transformed using the logit transformation. The heterogeneities of study-level estimates were evaluated by Cochran’s Q test and quantified using I2 statistics. p < .1 was considered the heterogeneity with statistically significant. I2 values of 25, 50, and 75% were considered low, moderate, and high heterogeneity, respectively [9]. A random-effects model was used to combine individual effect sizes to create pooled prevalence if a significantly high heterogeneity (Q statistic: p < .1 or I2>50%) was observed [10]. Otherwise, a fixed-effects model was utilized (Q-statistic: p > .1 and I2<50%) [11]. A funnel plot is widely used to judge assess the possibility of publication bias. If the funnel plot is asymmetric, publication bias may exist. However, in addition to publication bias, other factors may also contribute to funnel plot asymmetry [12], so we used a contour-enhanced funnel plot as an aid to differentiating asymmetry due to publication bias from that due to other factors [13]. The Egger test was used to assess the asymmetry of the funnel plot and p-value <.1 indicating significant publication bias [12]. All statistical analysis and plots were conducted using the ‘meta’ or ‘meta for’ package in R 4.1.2.
The efficacy of vitamin D in treatment of fibromyalgia: a meta-analysis of randomized controlled studies and systematic review
Published in Expert Review of Clinical Pharmacology, 2022
Kang Qu, Ming-Xi Li, Yu-Ling Zhou, Peng Yu, Ming Dong
This meta-analysis included fewer studies and may have potential publication bias, making it easier to draw positive conclusions. Typically, funnel plots are used to test meta-analyses for publication bias. The funnel plot method is a method to evaluate whether there is publication bias in meta-analysis by qualitatively determining whether the graph is symmetrical or not, which is highly subjective and different observers may obtain different conclusions [33]. Egger’s test is a quantitative method to evaluate the publication bias of meta-analysis, and the conclusion is more objective [34]. Since fewer studies were included in this meta-analysis, backing the impact on the judgment of funnel plot symmetry may also lead to insufficient test efficacy. Therefore, we chose Egger’s test for determining the presence or absence of publication bias. The results suggest that there was no significant publication bias in this meta-analysis (P = 0.93).