StandardMetaAnalysis {RobustBayesianCopas} | R Documentation |
Standard meta-analysis
Description
This function performs maximum likelihood estimation (MLE) of (\theta, \tau)
for the standard random effects meta-analysis model,
y_i = \theta + \tau u_i + s_i \epsilon_i,
where y_i
is the reported treatment effect for the i
th study, s_i
is the reported standard error for the i
th study, \theta
is the population treatment effect of interest, \tau > 0
is a heterogeneity parameter, and u_i
and \epsilon_i
are independent and distributed as N(0,1)
.
Usage
StandardMetaAnalysis(y, s, init = NULL, tol=1e-10, maxit=1000)
Arguments
y |
An |
s |
An |
init |
Optional initialization values for |
tol |
Convergence criterion for the optimization algorithm for finding the MLE. Default is |
maxit |
Maximum number of iterations for the optimization algorithm for finding the MLE. Default is |
Value
The function returns a list containing the following components:
theta.hat |
MLE of |
tau.hat |
MLE of |
H |
|
conv |
"1" if the optimization algorithm converged, "0" if algorithm did not converge. If |
References
Bai, R., Lin, L., Boland, M. R., and Chen, Y. (2020). "A robust Bayesian Copas selection model for quantifying and correcting publication bias." arXiv preprint arXiv:2005.02930.
Ning, J., Chen, Y., and Piao, J. (2017). "Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction." Biostatistics, 18(3):495-504.
Examples
############################################
# Example on the antidepressants data set. #
# This is from Section 6.2 of the paper by #
# Bai et al. (2020). #
############################################
# Load the full data
data(antidepressants)
attach(antidepressants)
# Extract the 50 published studies
published.data = antidepressants[which(antidepressants$Published==1),]
# Observed treatment effect
y.obs = published.data$Standardized_effect_size
# Observed standard error
s.obs = published.data$Standardized_SE
#################################
# Fit a standard meta-analysis #
# that ignores publication bias #
#################################
# Set seed
set.seed(123)
SMA.mod = StandardMetaAnalysis(y=y.obs, s=s.obs)
# Point estimate for theta
SMA.theta.hat = SMA.mod$theta.hat
# Use Hessian to estimate standard error for theta
SMA.Hessian = SMA.mod$H
# Standard error estimate for theta
SMA.theta.se = sqrt(SMA.Hessian[1,1])
# 95 percent confidence interval
SMA.interval = c(SMA.theta.hat-1.96*SMA.theta.se, SMA.theta.hat+1.96*SMA.theta.se)
# Display results
SMA.theta.hat
SMA.theta.se
SMA.interval