Dataset Information

Hybrid test for publication bias in meta-analysis.

ABSTRACT: Publication bias frequently appears in meta-analyses when the included studies' results (e.g., p-values) influence the studies' publication processes. Some unfavorable studies may be suppressed from publication, so the meta-analytic results may be biased toward an artificially favorable direction. Many statistical tests have been proposed to detect publication bias in recent two decades. However, they often make dramatically different assumptions about the cause of publication bias; therefore, they are usually powerful only in certain cases that support their particular assumptions, while their powers may be fairly low in many other cases. Although several simulation studies have been carried out to compare different tests' powers under various situations, it is typically infeasible to justify the exact mechanism of publication bias in a real-world meta-analysis and thus select the corresponding optimal publication bias test. We introduce a hybrid test for publication bias by synthesizing various tests and incorporating their benefits, so that it maintains relatively high powers across various mechanisms of publication bias. The superior performance of the proposed hybrid test is illustrated using simulation studies and three real-world meta-analyses with different effect sizes. It is compared with many existing methods, including the commonly used regression and rank tests, and the trim-and-fill method.

SUBMITTER: Lin L

PROVIDER: S-EPMC7434640 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Hybrid test for publication bias in meta-analysis.

Lin Lifeng L

Statistical methods in medical research 20200415 10

Publication bias frequently appears in meta-analyses when the included studies' results (e.g., <i>p</i>-values) influence the studies' publication processes. Some unfavorable studies may be suppressed from publication, so the meta-analytic results may be biased toward an artificially favorable direction. Many statistical tests have been proposed to detect publication bias in recent two decades. However, they often make dramatically different assumptions about the cause of publication bias; there ...[more]

PMID: 32290810

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Project description:The rank correlation test introduced by Begg and Mazumdar is extensively used in meta-analysis to test for publication bias in clinical and epidemiological studies. It is based on correlating the standardized treatment effect with the variance of the treatment effect using Kendall's tau as the measure of association. To our knowledge, the operational characteristics regarding the significance level of the test have not, however, been fully assessed.We propose an alternative rank correlation test to improve the error rates of the original Begg and Mazumdar test. This test is based on the simulated distribution of the estimated measure of association, conditional on sampling variances. Furthermore, Spearman's rho is suggested as an alternative rank correlation coefficient. The attained level and power of the tests are studied by simulations of meta-analyses assuming the fixed effects model.The significance levels of the original Begg and Mazumdar test often deviate considerably from the nominal level, the null hypothesis being rejected too infrequently. It is proven mathematically that the assumptions for using the rank correlation test are not strictly satisfied. The pairs of variables fail to be independent, and there is a correlation between the standardized effect sizes and sampling variances under the null hypothesis of no publication bias. In the meta-analysis setting, the adverse consequences of a false negative test are more profound than the disadvantages of a false positive test. Our alternative test improves the error rates in fixed effects meta-analysis. Its significance level equals the nominal value, and the Type II error rate is reduced. In small data sets Spearman's rho should be preferred to Kendall's tau as the measure of association.As the attained significance levels of the test introduced by Begg and Mazumdar often deviate greatly from the nominal level, modified rank correlation tests, improving the error rates, should be preferred when testing for publication bias assuming fixed effects meta-analysis.

Project description:Publication bias is a substantial problem for the credibility of research in general and of meta-analyses in particular, as it yields overestimated effects and may suggest the existence of non-existing effects. Although there is consensus that publication bias exists, how strongly it affects different scientific literatures is currently less well-known. We examined evidence of publication bias in a large-scale data set of primary studies that were included in 83 meta-analyses published in Psychological Bulletin (representing meta-analyses from psychology) and 499 systematic reviews from the Cochrane Database of Systematic Reviews (CDSR; representing meta-analyses from medicine). Publication bias was assessed on all homogeneous subsets (3.8% of all subsets of meta-analyses published in Psychological Bulletin) of primary studies included in meta-analyses, because publication bias methods do not have good statistical properties if the true effect size is heterogeneous. Publication bias tests did not reveal evidence for bias in the homogeneous subsets. Overestimation was minimal but statistically significant, providing evidence of publication bias that appeared to be similar in both fields. However, a Monte-Carlo simulation study revealed that the creation of homogeneous subsets resulted in challenging conditions for publication bias methods since the number of effect sizes in a subset was rather small (median number of effect sizes equaled 6). Our findings are in line with, in its most extreme case, publication bias ranging from no bias until only 5% statistically nonsignificant effect sizes being published. These and other findings, in combination with the small percentages of statistically significant primary effect sizes (28.9% and 18.9% for subsets published in Psychological Bulletin and CDSR), led to the conclusion that evidence for publication bias in the studied homogeneous subsets is weak, but suggestive of mild publication bias in both psychology and medicine.

Dataset Information

Hybrid test for publication bias in meta-analysis.

Publications

Hybrid test for publication bias in meta-analysis.

Similar Datasets

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