Introduction the pimeta package

Kengo Nagashima

2018-09-14

The pimeta package is easy. Load your data and then pass it the pima function!

require("pimeta")
data(sbp, package = "pimeta")

# a parametric bootstrap prediction interval
set.seed(20161102)
pima(y  = sbp$y,      # effect size estimates
     se = sbp$sigmak, # within studies standard errors
     B  = 50000       # some options (e.g., number of bootstrap samples)
)
Prediction Interval for Random-Effects Meta-Analysis

A parametric bootstrap prediction interval
 Heterogeneity variance: DerSimonian-Laird
 SE for average treatment effect: standard

Average treatment effect [95%PI]:
 -0.3341 [-0.8769, 0.2248]

Average treatment effect [95%CI]:
 -0.3341 [-0.5660, -0.0976]

Heterogeneity variance (tau^2):
 0.0282

Several type of methods is supported.

# Higgins-Thompson-Spiegelhalter prediction interval
pima(sbp$y, sbp$sigmak, method = "HTS")

# Partlett-Riley prediction interval (Hartung and Knapp's SE)
pima(sbp$y, sbp$sigmak, method = "HK")

# Partlett-Riley prediction interval (Sidik and Jonkman's SE)
pima(sbp$y, sbp$sigmak, method = "SJ")