pimeta (version 1.1.1)

pima_htsreml: Partlett--Riley prediction interval

Description

A subroutine for the Partlett--Riley PI based on the REML estimator (Partlett & Riley, 2017)

Usage

pima_htsreml(y, sigma, alpha = 0.05, vartype = c("HK", "SJBC", "CL"),
  maxiter = 100)

Arguments

y

the effect size estimates vector

sigma

the within studies standard errors vector

alpha

the alpha level of the prediction interval

vartype

the type of the variance estimator for \(\hat{\mu}\) (default = "HK"):

• HK: the Hartung and Knapp (2001)'s estimator.

• SJBC: the Sidik and Jonkman (2006)'s bias coreccted estimator.

• CL: a standard estimator, \((1/\sum{\hat{w}_i})^{-1}\).

maxiter

the maximum number of iterations

Value

• muhat: the average treatment effect estimate \(\hat{\mu}\).

• lci, lci: the lower and upper confidence limits \(\hat{\mu}_l\) and \(\hat{\mu}_u\).

• lpi, lpi: the lower and upper prediction limits \(\hat{c}_l\) and \(\hat{c}_u\).

• tau2h: the estimate for \(\tau^2\).

References

Partlett, C, and Riley, R. D. (2017). Random effects meta-analysis: Coverage performance of 95 confidence and prediction intervals following REML estimation. Stat Med. 36(2): 301-317.

See Also

pima().

Examples

# NOT RUN {
data(sbp, package = "pimeta")
pimeta::pima_htsreml(sbp$y, sbp$sigmak)
# 
# Prediction Interval for Random-Effects Meta-Analysis
# 
# Partlett-Riley prediction interval
#  Heterogeneity variance: REML
#  SE for average treatment effect: Hartung-Knapp
# 
# Average treatment effect [95%PI]:
#  -0.3287 [-0.9887, 0.3312]
# 
# Average treatment effect [95%CI]:
#  -0.3287 [-0.5761, -0.0814]
# 
# Heterogeneity variance (tau^2):
#  0.0700
#
# }