Kavli Affiliate: Yi Zhou
| First 5 Authors: Taojun Hu, Yi Zhou, Satoshi Hattori, ,
| Summary:
Meta-analysis is a powerful tool to synthesize findings from multiple
studies. The normal-normal random-effects model is widely used to account for
between-study heterogeneity. However, meta-analysis of sparse data, which may
arise when the event rate is low for binary or count outcomes, poses a
challenge to the normal-normal random-effects model in the accuracy and
stability in inference since the normal approximation in the within-study model
may not be good. To reduce bias arising from data sparsity, the generalized
linear mixed model can be used by replacing the approximate normal within-study
model with an exact model. Publication bias is one of the most serious threats
in meta-analysis. Several quantitative sensitivity analysis methods for
evaluating the potential impacts of selective publication are available for the
normal-normal random-effects model. We propose a sensitivity analysis method by
extending the likelihood-based sensitivity analysis with the t-statistic
selection function of Copas to several generalized linear mixed-effects models.
Through applications of our proposed method to several real-world meta-analysis
and simulation studies, the proposed method was proven to outperform the
likelihood-based sensitivity analysis based on the normal-normal model. The
proposed method would give useful guidance to address publication bias in
meta-analysis of sparse data.
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