Kavli Affiliate: Yi Zhou
| First 5 Authors: , , , ,
| Summary:
In systematic reviews and meta-analyses, publication bias (PB) is one of the
serious concerns and mainly induced by selective publication of academic
literatures. Although many methods have been proposed to deal with PB, almost
all the methods are based on the normal-normal (NN) random-effects model
assuming that data are normally distributed in both the within-study and the
between-study levels. For rare-event meta-analysis where data contain rare
occurrences of events, the standard NN random-effects model may perform poorly.
Instead, some generalized linear mixed models (GLMMs) which employ the exact
distribution for the number of events in within-study level provide
alternatives and have been widely used in practice. However, limited methods
can be applied to deal with PB in the GLMMs. To address this limitation, we
propose a framework of sensitivity analysis for evaluating the impact of PB in
various GLMMs. The proposed framework is developed based on the famous
Copas-Heckman-type sensitivity analysis methods and can be easily implemented
with the standard software with small computational cost. In this paper, we
conduct simulation studies to assess the performance of proposed methods in
adjusting PB and compare the results with related existing methods. Several
real-world examples are also analyzed to show the broad applicability of our
proposal in evaluating the potential impact of PB in meta-analysis of odds
ratios and proportions with rare-event outcomes.
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