pimeta (version 1.1.1)

pima_boot: A parametric bootstrap prediction interval

Description

A subroutine for the parametric bootstrap PI based on confidence distribution (Nagashima et al., 2018). A parametric bootstrap confidence interval is also calculated based on similar sampling method.

Usage

pima_boot(y, sigma, alpha = 0.05, B = 25000, maxit1 = 1e+05,
  eps = 10^(-10), lower = 0, upper = 1000, maxit2 = 1000,
  tol = .Machine$double.eps^0.25, rnd = NULL)

Arguments

y

the effect size estimates vector

sigma

the within studies standard errors vector

alpha

the alpha level of the prediction interval

B

the number of bootstrap samples

maxit1

the maximum number of iteration for the exact distribution function of \(Q\)

eps

the desired level of accuracy for the exact distribution function of \(Q\)

lower

the lower limit of random numbers of \(\tau^2\)

upper

the lower upper of random numbers of \(\tau^2\)

maxit2

the maximum number of iteration for numerical inversions

tol

the desired level of accuracy for numerical inversions

rnd

a vector of random numbers from the exact distribution of \(\tau^2\)

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

Nagashima, K., Noma, H., and Furukawa, T. A. (2018). Prediction intervals for random-effects meta-analysis: a confidence distribution approach. Stat Methods Med Res. In press. https://doi.org/10.1177/0962280218773520.

See Also

pima().

Examples

# NOT RUN {
data(sbp, package = "pimeta")
set.seed(20161102)
# }
# NOT RUN {
pimeta::pima_boot(sbp$y, sbp$sigmak, B = 50000)
# }
# NOT RUN {
# 
# Prediction Interval for Random-Effects Meta-Analysis
# 
# A parametric bootstrap prediction interval
#  Heterogeneity variance: DerSimonian-Laird
#  SE for average treatment effect: Hartung
# 
# 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
# 
# }