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Abstract
BackgroundMeta-analysis provides a useful statistical tool to effectively estimate treatment effect from multiple studies. When the outcome is binary and it is rare (e.g., safety data in clinical trials), the traditionally used methods may have unsatisfactory performance.MethodsWe propose using importance sampling to compute confidence intervals for risk difference in meta-analysis with rare events. The proposed intervals are not exact, but they often have the coverage probabilities close to the nominal level. We compare the proposed accurate intervals with the existing intervals from the fixed- or random-effects models and the interval by Tian et al. (2009).ResultsWe conduct extensive simulation studies to compare them with regards to coverage probability and average length, when data are simulated under the homogeneity or heterogeneity assumption of study effects.ConclusionsThe proposed accurate interval based on the random-effects model for sample space ordering generally has satisfactory performance under the heterogeneity assumption, while the traditionally used interval based on the fixed-effects model works well when the studies are homogeneous.
Highlights
Meta-analysis provides a useful statistical tool to effectively estimate treatment effect from multiple studies
These 18 schizophrenia clinical trials reported the number of all-cause mortality for patients treated with the long-acting injectable antipsychotics (LAI-AP) or the oral antipsychotics (OAP) which is the control treatment here
The importance sampling (IS)-R interval based on the asymptotic limits from the random-effects model outperforms the existing intervals under the heterogeneity assumption
Summary
Meta-analysis provides a useful statistical tool to effectively estimate treatment effect from multiple studies. The fixedeffects models are conveniently used in practice, such as the Mantel-Haenszel method [5] When one or both groups in a study have zero events, a continuity correction is often needed in order to estimate risk ratio or odds ratio, but the traditional correction by adding 0.5 may lead to undesirable influence on the analysis results as pointed out by Sweeting et al [6]. Later, they developed a continuity correction method by adding a float value based on the size of each group to improve the coverage probability. Kuss et al [8] suggested using a beta-binomial model to avoid adding
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