Prediction interval for random-effects meta-analysis: a confidence distribution approach

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Authors Kengo Nagashima, Hisashi Noma, Toshiaki Furukawa
Journal/Conference Name Statistical methods in medical research
Paper Category
Paper Abstract 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.
Date of publication 2019
Code Programming Language R
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