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AuthorXu, Chang
AuthorFuruya-Kanamori, Luis
AuthorLin, Lifeng
Available date2021-08-31T07:31:53Z
Publication Date2021-01-01
Publication NameResearch Synthesis Methods
Identifierhttp://dx.doi.org/10.1002/jrsm.1521
CitationXu, C, Furuya-Kanamori, L, Lin, L. Synthesis of evidence from zero-events studies: A comparison of one-stage framework methods. Res Syn Meth. 2021; 1- 14. doi:10.1002/jrsm.1521
ISSN1759-2879
URIhttp://hdl.handle.net/10576/22312
AbstractIn evidence synthesis, dealing with zero-events studies is an important and complicated task that has generated broad discussion. Numerous methods provide valid solutions to synthesizing data from studies with zero-events, either based on a frequentist or a Bayesian framework. Among frequentist frameworks, the one-stage methods have their unique advantages to deal with zero-events studies, especially for double-arm-zero-events. In this article, we give a concise overview of the one-stage frequentist methods. We conducted simulation studies to compare the statistical properties of these methods to the two-stage frequentist method (continuity correction) for meta-analysis with zero-events studies when double-zero-events studies were included. Our simulation studies demonstrated that the generalized estimating equation with unstructured correlation and beta-binomial method had the best performance among the one-stage methods. The random intercepts generalized linear mixed model showed good performance in the absence of obvious between-study variance. Our results also showed that the continuity correction with inverse-variance heterogeneous (IVhet) analytic model based on the two-stage framework had good performance when the between-study variance was obvious and the group size was balanced for included studies. In summary, the one-stage framework has unique advantages to deal with studies with zero events and is not susceptive to group size ratio. It should be considered in future meta-analyses whenever possible.
Languageen
PublisherJohn Wiley & Sons, Ltd
Subjectbeta-binomial model
generalized estimating equation
generalized linear mixed model
meta-analysis
zero-events study
TitleSynthesis of evidence from zero-events studies: A comparison of one-stage framework methods
TypeArticle
ESSN1759-2887


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