| Title: | Efficient Numerical Algorithm for Exact Inference in Meta Analysis |
|---|---|
| Description: | A novel numerical algorithm that provides functionality for estimating the exact 95% confidence interval of the location parameter in the random effects model, and is much faster than the naive method. Works best when the number of studies is between 6-20. |
| Authors: | Grace Deng and Lu Tian |
| Maintainer: | Lu Tian <[email protected]> |
| License: | GPL-2 |
| Version: | 0.1.0 |
| Built: | 2024-12-06 06:49:01 UTC |
| Source: | CRAN |
Computes the point estimator for the center (theta), the p value for testing if the center is zero, and the 95% confidence interval in a random effects model meta analysis. When the number of studies is moderate or small (<=20), the exact inference results are based on the exact computation. When the number of studies is big (>20), the exact inference results are based on Monte-Carlo simulation.
random.meta(y, v, type="DL", B=500, N=10000, Bstep=5, plot.meta=T)random.meta(y, v, type="DL", B=500, N=10000, Bstep=5, plot.meta=T)
y |
A vector of the respective estimators of the study-specific effect from each study. Length should be the same as the number of studies. |
v |
A vector with the variance of each estimator in y. Length should be the same as the number of studies. |
type |
The test method to be used for constructing the CI, choosing from "DL", "wang", "median" and "wilcox". The default is "DL". |
B |
The number of grids used to construct the 95% CI. The default value is 500. |
N |
The number of simulations in the Monte-Carlo simulation. The default value is 10000. |
Bstep |
The number of steps used in searching the endpoint of the 95% CI. The default value is 5, which the user does not need to adjust. A larger value may slow down computation. |
plot.meta |
The logic value for generating the forest plot of the meta analysis. The default value is "TRUE". |
The inference results are "exact" if K <= 20 and based on Monte-Carlo simulation if K > 20.
theta |
The point estimator for the center |
pvalue |
The p value for testing if the center is zero |
ci95 |
The 95% CI for the center |
Lu Tian and Grace Deng
Sifan Liu, Lu Tian, Steve Lee and Min-ge Xie (2016) Exact inference on meta-analysis with generalized fixed-effects and random-effects models. Tech Report.\
Yan Wang and Lu Tian (2017) An efficient numerical algorithm for exact inference in meta analysis. Tech Report.
##### Generate the data for a meta analysis with 8 studies ##### set.seed(100) K=8 tau=2 v=rchisq(K, 3) y=rnorm(K)*sqrt(v+tau)+1 ##### Exact inference using the DL method ##### fit=random.meta(y, v, type="DL") fit ##### Exact inference using the Wilcoxon method ##### fit=random.meta(y, v, type="wilcox") fit##### Generate the data for a meta analysis with 8 studies ##### set.seed(100) K=8 tau=2 v=rchisq(K, 3) y=rnorm(K)*sqrt(v+tau)+1 ##### Exact inference using the DL method ##### fit=random.meta(y, v, type="DL") fit ##### Exact inference using the Wilcoxon method ##### fit=random.meta(y, v, type="wilcox") fit