Prediction Intervals for Random-Effects Meta-Analysis

An implementation of prediction intervals for random-effects meta-analysis: Higgins et al. (2009) , Partlett and Riley (2017) , and Nagashima et al. (2018) , .


Prediction Intervals for Random-Effects Meta-Analysis

An implementation of prediction intervals for random-effects meta-analysis:

  • Higgins JPT, Thompson SG, Spiegelhalter DJ. A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2009; 172(1): 137--159. doi:10.1111/j.1467-985X.2008.00552.x.
  • Partlett C, Riley RD. Random effects meta-analysis: Coverage performance of 95% confidence and prediction intervals following REML estimation. Statistics in Medicine 2017; 36(2): 301--317. doi:10.1002/sim.7140.
  • Nagashima K, Noma H, Furukawa TA. Prediction interval for random-effects meta-analysis: a confidence distribution approach. Statistical Methods in Medical Research 2018. In press. doi:10.1177/0962280218773520. arXiv:1804.01054.

CRAN task view: Meta Analysis

Installation

# From CRAN:
install.packages("pimeta")
 
# Or the the development version from GitHub:
# install.packages("devtools")
devtools::install_github("nshi-stat/pimeta")

News

The 'pimeta' package

Version 1.1.2 (2019-03-11)

  • Prediction interval: the pima function
    • Parallel computing for the parametric bootstrap method (see a Vignette file).
    • Forest plot (see a Vignette file).
    • Kenward-Roger's approach.
  • Confidence interval: the cima function
    • A Wald-type t-distribution confidence interval. Variance estimator of the average effect: an approximate estimator. Heterogeneity variance: Dersimonian-Laird estimator.
    • A Wald-type t-distribution confidence interval. Variance estimator of the average effect: an approximate, Hartung-Knapp, Sidik-Jonkman, Kenward-Roger estimators. Heterogeneity variance: REML estimator.
    • Profile likelihood confidence interval.
    • Profile likelihood confidence interval with a Bartlett type correction.
    • Forest plot.
  • Heterogeneity variance estimators: the tau2h function
    • DerSimonian-Laird estimator.
    • Variance component type estimator.
    • Paule--Mandel estimator.
    • Hartung-Makambi estimator.
    • Hunter--Schmidt estimator.
    • Maximum likelihood estimator.
    • Restricted maximum likelihood estimator.
    • Approximate restricted maximum likelihood estimator.
    • Sidik--Jonkman estimator.
    • Sidik--Jonkman improved estimator.
    • Empirical Bayes estimator.
    • Bayes modal estimator.
    • ML and REML confidence intervals.
  • Converting binary data: the convert_bin function
    • Converting binary data to logarithmic odds ratio (see a Vignette file).
    • Converting binary data to logarithmic relative risk.
    • Converting binary data to risk difference.
  • The distribution of a positive linear combination of chiqaure random variables: the pwchisq function

Version 1.1.1 (2018-09-15)

  • Fixed documents.

Version 1.1.0 (2018-09-14)

  • Refined the package structure.
  • New function pima is available (see a Vignette file).

Version 1.0.1 (2018-05-11)

  • Updated citation informations.

Version 1.0.0 (2018-04-05)

  • First release.

Reference manual

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install.packages("pimeta")

1.1.2 by Kengo Nagashima, 6 months ago


Browse source code at https://github.com/cran/pimeta


Authors: Kengo Nagashima [aut, cre] , Hisashi Noma [aut] , Toshi A. Furukawa [aut]


Documentation:   PDF Manual  


Task views: Meta-Analysis


GPL-3 license


Imports stats, Rcpp, ggplot2, utils

Suggests knitr, rmarkdown

Linking to Rcpp, RcppEigen


See at CRAN