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Nonparametric estimation of effect heterogeneity in rare events meta-analysis: bivariate, discrete mixture model. (English) Zbl 1464.62291

Summary: Meta-analysis provides an integrated analysis and summary of the effects observed in \(k\) independent studies. The conventional analysis proceeds by first calculating a study-specific effect estimate, and then provides further analysis on the basis of the available \(k\) independent effect estimates associated with their uncertainty measures. Here we consider a setting where counts of events are available from \(k\) independent studies for a treatment and a control group. We suggest to model this situation with a study-specific Poisson regression model, and allow the study-specific parameters of the Poisson model to arise from a nonparametric mixture model. This approach then allows the estimation of the heterogeneity variance of the effect measure of interest in a nonparametric manner. A case study is used to illustrate the methodology throughout the paper.

MSC:

62H12 Estimation in multivariate analysis
62G05 Nonparametric estimation
62G32 Statistics of extreme values; tail inference
62P10 Applications of statistics to biology and medical sciences; meta analysis
92C60 Medical epidemiology

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References:

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