An efficient numerical algorithm for exact inference in meta analysis

The performance of commonly used asymptotic inference procedures for the random-effects model used in meta analysis relies on the number of studies. When the number of studies is moderate or small, the exact inference procedure is more reliable than the asymptotic counterparts. However, the related...

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Bibliographic Details
Published in:Journal of statistical computation and simulation Vol. 88; no. 4; pp. 646 - 656
Main Authors: Wang, Yan, Tian, Lu
Format: Journal Article
Language:English
Published: United States Taylor & Francis 04.03.2018
Taylor & Francis Ltd
Subjects:
Algorithms
Asymptotic methods
Comparative studies
Confidence intervals
exact inference
Inference
Mathematical models
Meta analysis
Numerical analysis
random-effects model
exact inference
Meta analysis
random effects model
ISSN:0094-9655, 1563-5163
Online Access:Get full text
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Summary:The performance of commonly used asymptotic inference procedures for the random-effects model used in meta analysis relies on the number of studies. When the number of studies is moderate or small, the exact inference procedure is more reliable than the asymptotic counterparts. However, the related numerical computation may be demanding and an obstacle of routine use of the exact method. In this paper, we proposed a novel numerical algorithm for constructing the exact 95% confidence interval of the location parameter in the random-effects model. The algorithm is much faster than the naive method and may greatly facilitate the use of the more appropriate exact inference procedure in meta analysis. Numerical studies and real data examples are used to illustrate the advantage of the proposed method.
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ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2017.1402331