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A simple method to estimate prediction intervals and predictive distributions: Summarizing meta‐analyses beyond means and confidence intervals
Author(s) -
Wang ChiaChun,
Lee WenChung
Publication year - 2019
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1345
Subject(s) - meta analysis , confidence interval , prediction interval , statistics , confidence distribution , computer science , coverage probability , nonparametric statistics , range (aeronautics) , cdf based nonparametric confidence interval , study heterogeneity , econometrics , mathematics , medicine , materials science , composite material
A systematic review and meta‐analysis is an important step in evidence synthesis. The current paradigm for meta‐analyses requires a presentation of the means under a random‐effects model; however, a mean with a confidence interval provides an incomplete summary of the underlying heterogeneity in meta‐analysis. Prediction intervals show the range of true effects in future studies and have been advocated to be regularly presented. Most commonly, prediction intervals are estimated assuming that the underlying heterogeneity follows a normal distribution, which is not necessarily appropriate. In this article, we provide a simple method with a ready‐to‐use spreadsheet file to estimate prediction intervals and predictive distributions nonparametrically. Simulation studies show that this new method can provide approximately unbiased estimates compared with the conventional method. We also illustrate the advantage and real‐world significance of this approach with a meta‐analysis evaluating the protective effect of vaccination against tuberculosis. The nonparametric predictive distribution provides more information about the shape of the underlying distribution than does the conventional method.