Description Usage Arguments Details Value References See Also Examples
This function can estimate prediction intervals (PIs) as follows: A parametric bootstrap PI based on confidence distribution (Nagashima et al., 2018). A parametric bootstrap confidence interval is also calculated based on the same sampling method for bootstrap PI. The Higgins–Thompson–Spiegelhalter (2009) prediction interval. The Partlett–Riley (2017) prediction intervals.
1 2 3 4 5 | pima(y, se, v = NULL, alpha = 0.05, method = c("boot", "HTS", "HK",
"SJ", "KR", "CL", "APX", "WL"), theta0 = 0, side = c("lt", "gt"),
B = 25000, parallel = FALSE, seed = NULL, maxit1 = 1e+05,
eps = 10^(-10), lower = 0, upper = 1000, maxit2 = 1000,
tol = .Machine$double.eps^0.25, rnd = NULL, maxiter = 100)
|
y |
the effect size estimates vector |
se |
the within studies standard error estimates vector |
v |
the within studies variance estimates vector |
alpha |
the alpha level of the prediction interval |
method |
the calculation method for the pretiction interval (default = "boot").
|
theta0 |
threshold θ_0, for the cumulative probability of effect θ_{new} less or greater than θ_0; \Pr(θ_{new} < θ_0) or \Pr(θ_{new} > θ_0). |
side |
either the cumulative probability of effect less (default = "lt") or greater ("gt") then θ_0 |
B |
the number of bootstrap samples |
parallel |
the number of threads used in parallel computing, or FALSE that means single threading |
seed |
set the value of random seed |
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 τ^2 |
upper |
the upper limit of random numbers of τ^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 τ^2 |
maxiter |
the maximum number of iteration for REML estimation |
The functions bootPI
, pima_boot
,
pima_hts
, htsdl
, pima_htsreml
, htsreml
are deprecated, and integrated to the pima
function.
K
: the number of studies.
muhat
: the average treatment effect estimate \hat{μ}.
lci
, uci
: the lower and upper confidence limits \hat{μ}_l and \hat{μ}_u.
lpi
, upi
: the lower and upper prediction limits \hat{c}_l and \hat{c}_u.
tau2h
: the estimate for τ^2.
i2h
: the estimate for I^2.
nup
: degrees of freedom for the prediction interval.
nuc
: degrees of freedom for the confidence interval.
vmuhat
: the variance estimate for \hat{μ}.
Higgins, J. P. T, Thompson, S. G., Spiegelhalter, D. J. (2009). A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A Stat Soc. 172(1): 137-159. https://doi.org/10.1111/j.1467-985X.2008.00552.x
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. https://doi.org/10.1002/sim.7140
Nagashima, K., Noma, H., and Furukawa, T. A. (2019). Prediction intervals for random-effects meta-analysis: a confidence distribution approach. Stat Methods Med Res. 28(6): 1689-1702. https://doi.org/10.1177/0962280218773520.
Wang, C-C and Lee, W-C. (2019). A simple method to estimate prediction intervals and predictive distributions. Res Syn Meth. 30(28): 3304-3312. https://doi.org/10.1002/jrsm.1345.
Hartung, J. (1999). An alternative method for meta-analysis. Biom J. 41(8): 901-916. https://doi.org/10.1002/(SICI)1521-4036(199912)41:8<901::AID-BIMJ901>3.0.CO;2-W.
Hartung, J., and Knapp, G. (2001). On tests of the overall treatment effect in meta-analysis with normally distributed responses. Stat Med. 20(12): 1771-1782. https://doi.org/10.1002/sim.791.
Sidik, K., and Jonkman, J. N. (2006). Robust variance estimation for random effects meta-analysis. Comput Stat Data Anal. 50(12): 3681-3701. https://doi.org/10.1016/j.csda.2005.07.019.
Kenward, M. G., and Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics. 53(3): 983-997. https://www.ncbi.nlm.nih.gov/pubmed/9333350.
DerSimonian, R., and Laird, N. (1986). Meta-analysis in clinical trials. Control Clin Trials. 7(3): 177-188.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | data(sbp, package = "pimeta")
# Nagashima-Noma-Furukawa prediction interval
# is sufficiently accurate when I^2 >= 10% and K >= 3
pimeta::pima(sbp$y, sbp$sigmak, seed = 3141592, parallel = 4)
# Higgins-Thompson-Spiegelhalter prediction interval and
# Partlett-Riley prediction intervals
# are accurate when I^2 > 30% and K > 25
pimeta::pima(sbp$y, sbp$sigmak, method = "HTS")
pimeta::pima(sbp$y, sbp$sigmak, method = "HK")
pimeta::pima(sbp$y, sbp$sigmak, method = "SJ")
pimeta::pima(sbp$y, sbp$sigmak, method = "KR")
pimeta::pima(sbp$y, sbp$sigmak, method = "APX")
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.