Bias in meta-analysis detected by a simple, graphical test. Graphical test is itself biased (Letter)

BMJ Clinical Research (Impact Factor: 14.09). 03/1998; 316(7129):470; author reply 470-1.
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Available from: Paul Glasziou,
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    • "Small-study effects were examined using the regression approach of Egger et al. [23]. Small-study effects may be due to publication bias, selective reporting of outcomes [24, 25], true heterogeneity [23, 26], artifacts [27], or chance [28]. Two-tailed 95% confidence intervals that did not include zero (0) were considered to be suggestive of small-study effects. "
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    ABSTRACT: Fibromyalgia is a major public health problem affecting an estimated 200 to 400 million people worldwide. The purpose of this study was to use the meta-analytic approach to determine the efficacy and effectiveness of randomized controlled exercise intervention trials (aerobic, strength training, or both) on tender points (TPs) in adults with fibromyalgia. Using random effects models and 95% confidence intervals (CI), a statistically significant reduction in TPs was observed based on per-protocol analyses (8 studies representing 322 participants) but not intention-to-treat analyses (5 studies representing 338 participants) (per-protocol, g, -0.68, 95% CI, -1.16, -0.20; intention-to-treat, g, -0.24, 95% CI, -0.62, 0.15). Changes were equivalent to relative reductions of 10.9% and 6.9%, respectively, for per-protocol and intention-to-treat analyses. It was concluded that exercise is efficacious for reducing TPs in women with FM. However, a need exists for additional well-designed and reported studies on this topic.
    10/2011; 2011:125485. DOI:10.1155/2011/125485
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    • "It detects bias based on the assumption that the plot resembles a symmetrical inverted funnel in the absence of bias (Egger et al 1997a). Many factors may potentially contribute to the detected asymmetry; therefore, this method should be used with caution, especially when limited numbers of studies are used in a meta-analysis (Irwig et al 1998; Stuck et al 1998; Vandenbroucke 1998). Detection of asymmetry in a funnel plot can be conducted by several methods, such as visual inspection, " trim and fill " , regression approach (Soeken and Sripusanapan 2003), and a newly emerged method (Formann 2008) in which the proportion of unpublished studies is estimated by the degree of truncation from a left-truncated normal distribution. "
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    ABSTRACT: Publication bias has been around for about 50 years. It has become a concern for almost 20 years in the medical research community. This review briefly summarizes the current status of publication bias, potential sources where bias may arise from, and its common evaluation methods. In the field of translational stroke research, publication bias has long been suspected; however, it has not been addressed with sufficient efforts. Its status has remained the same during the last decade. The author emphasizes the important role that publishers might play in addressing publication bias.
    Journal of Experimental Stroke and Translational Medicine 01/2009; 2(1):16-21. DOI:10.6030/1939-067X-2.1.16
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    • "However, the procedure lacks empirical evidence to support what is observed is, indeed, selection bias. It was suggested that the method may result in false-positive results [9]. The validity of funnel plots needs to be scrutinized. "
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    ABSTRACT: Publication and other forms of selection biases pose a threat to the validity of meta-analysis. Funnel plots are usually used to detect such biases; asymmetrical plots are interpreted to suggest that biases are present. Using 198 published meta-analyses, we demonstrate that the shape of a funnel plot is largely determined by the arbitrary choice of the method to construct the plot. When a different definition of precision and/or effect measure were used, the conclusion about the shape of the plot was altered in 37 (86%) of the 43 meta-analyses with an asymmetrical plot suggesting selection bias. In the absence of a consensus on how the plot should be constructed, asymmetrical funnel plots should be interpreted cautiously. These findings also suggest that the discrepancies between large trials and corresponding meta-analyses and heterogeneity in meta-analyses may also be determined by how they are evaluated.
    Journal of Clinical Epidemiology 06/2000; 53(5):477-84. DOI:10.1016/S0895-4356(99)00204-8 · 3.42 Impact Factor
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