ArticleContemporary clinical trials communications2021
Tests for publication bias are unreliable in case of heteroscedasticity.
Article in Contemporary clinical trials communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meta-analysis of corm yield responses to chemical and biological approaches for managing taro leaf blight disease.Pest management science · 2026Pooled it
- The potential of IFN-λ, IL-32γ, IL-6, and IL-22 as safeguards against human viruses: a systematic review and a meta-analysis.Frontiers in immunology · 2024Pooled it
- Rating Down for Publication Bias is Rare in Cochrane Reviews: A Descriptive Study.Cochrane evidence synthesis and methods · 2026Article
- Review
- Combined bailing capsule and conventional therapies in the treatment of chronic renal failure: a meta-analysis and economic evaluation.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Regression based methods for the detection of publication bias in meta-analysis have been extensively evaluated in literature. When dealing with continuous outcomes, specific hidden factors (e.g., heteroscedasticity) may interfere with the test statistics. In this paper we investigate the influence of residual heteroscedasticity on the performance of four tests for publication bias: the Egger test, the Begg-Mazumdar test and two tests based on weighted regression. In the presence of heteroscedasticity, the Egger test and the weighted regression tests highly inflate the Type I error rate, while the Begg-Mazumdar test deflates the Type I error rate. Although all three tests already have low statistical power, heteroscedasticity typically reduces it further. Our results in combination with earlier discussions on publication bias tests lead us to conclude that application of these tests on continuous treatment effects is not warranted.
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