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A Bound for Publication Bias Based on the Fraction of Unpublished Studies
Author(s) -
Copas John,
Jackson Dan
Publication year - 2004
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2004.00161.x
Subject(s) - fraction (chemistry) , publication bias , computer science , information retrieval , statistics , data science , mathematics , confidence interval , chemistry , chromatography
Summary. Publication bias in meta‐analysis is usually modeled in terms of an accept/reject selection procedure in which the selected studies are the “published” studies and the rejected studies are the “unpublished” studies. One possible selection mechanism is to suppose that only studies that report an estimated treatment effect exceeding (or falling short of) some threshold are accepted. We show that, with appropriate choice of thresholds, this attains the maximum bias among all selection mechanisms in which the probability of selection increases with study size. It is impossible to estimate the selection mechanism from the observed studies alone: this result leads to a “worst‐case” sensitivity analysis for publication bias, which is remarkably easy to implement in practice. The method is illustrated using data on the effectiveness of prophylactic corticosteroids.