Papers › Prediction intervals for random-effects meta-analysis: a confidence distribution approach
Prediction intervals for random-effects meta-analysis: a confidence distribution approach
Kengo Nagashima, Hisashi Noma, Toshi A. Furukawa
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Prediction intervals are commonly used in meta-analysis with random-effects models. One widely used method, the Higgins-Thompson-Spiegelhalter prediction interval, replaces the heterogeneity parameter with its point estimate, but its validity strongly depends on a large sample approximation. This is a weakness in meta-analyses with few studies. We propose an alternative based on bootstrap and show by simulations that its coverage is close to the nominal level, unlike the Higgins-Thompson-Spiegelhalter method and its extensions. The proposed method was applied in three meta-analyses.
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