summary.multipletables: Summary a specific study of objects `multipletables`

Description

Summary a model of class multipletables fitted by multipletables.

Usage

# S3 method for multipletables
summary(object,verbose=TRUE,...)

Value

A list with the following components:

model: the value of model argument.
measure: the value of measure argument.
cov.matrix: the estimated covariance matrix of the estimated parameters in the transformed scales
hessian: the estimated hessian matrix of the estimated parameters in the transformed scales
overall: a list of two components that contain the overall measure (e.g., overall OR) and its 95% equal-tail credible interval.
studynames: a character string indicating all the study names
chi2: the chi-square test statistics of the likelihood ratio test
pvalue: the p-value of the likelihood ratio test
alpha: the value of alpha argument.
MLE: a numeric vector of the estimated hyperparameters in the following order: a1, b1, a2, b2, rho.
studyspecific: a Numeric matrix with columns being the posterior means, the lower bound, and the upper bound of the credible/confidence intervals of study-specific and overall measure.

Arguments

object: an object inheriting from class multipletables.
verbose: a logical value; if TRUE(default), the detailed summary messages will display.
...: additional arguments; currently none is used.

References

Luo, S., Chen, Y., Su, X., Chu, H., (2014). mmeta: An R Package for Multivariate Meta-Analysis.
Journal of Statistical Software, 56(11), 1-26.
<https://dukespace.lib.duke.edu/dspace/bitstream/handle/10161/15522/2014Luo_Chen_Su_Chu_JSS_mmeta.pdf?sequence=1>

Chen, Y., Luo, S., (2011a). A Few Remarks on "Statistical Distribution of the Difference of Two Proportions' by Nadarajah and Kotz, Statistics in Medicine 2007; 26(18):3518-3523".
Statistics in Medicine, 30(15), 1913-1915.
<doi:10.1002/sim.4248>

Chen, Y., Chu, H., Luo, S., Nie, L., and Chen, S. (2014a). Bayesian analysis on meta-analysis of case-control studies accounting for within-study correlation.
Statistical Methods in Medical Research, 4.6 (2015): 836-855.
<https://doi.org/10.1177/0962280211430889>.

Chen, Y., Luo, S., Chu, H., Su, X., and Nie, L. (2014b). An empirical Bayes method for multivariate meta-analysis with an application in clinical trials.
Communication in Statistics: Theory and Methods, 43.16 (2014): 3536-3551.
<https://doi.org/10.1080/03610926.2012.700379>.

Chen, Y., Luo, S., Chu, H., Wei, P. (2013). Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.
Statistics in Biopharmaceutical Research, 5(2), 142-155.
<https://doi.org/10.1080/19466315.2013.791483>.

Examples

Run this code

# \donttest{
library(mmeta)

# Analyze the dataset colorectal to conduct exact inference of the odds ratios
data(colorectal)
multiple.OR <- multipletables(data=colorectal, measure="OR", model="Sarmanov", method="exact")
# Generate the forest plot with 95% CIs of study-specific odds ratios
# and 95% CI of overall odds ratio
plot(multiple.OR, type="forest", addline=1)
# Plot the posterior density functions of some target studies
# in an overlaying manner
plot(multiple.OR, type="overlap", select=c(4,14,16,20))
# Plot the posterior density functions of some target studies 
# in a side-by-side manner 
plot(multiple.OR, type="sidebyside", select=c(4,14,16,20), ylim=c(0,2.7), xlim=c(0.5,1.5))

# Analyze the dataset withdrawal to conduct inference of the relative risks
data(withdrawal)
multiple.RR <- multipletables(data=withdrawal, measure="RR",model="Sarmanov")
plot(multiple.RR, type="forest", addline=1)
plot(multiple.RR, type="overlap", select=c(3,8,14,16))
plot(multiple.RR, type="sidebyside", select=c(3,8,14,16), ylim=c(0,1.2),xlim=c(0.4,3))

# Analyze the dataset withdrawal to conduct inference of the risk differences
data(withdrawal)
multiple.RD <- multipletables(data=withdrawal, measure="RD",
                              model="Sarmanov")
summary(multiple.RD)
plot(multiple.RD, type="forest", addline=0)
plot(multiple.RD, type="overlap", select=c(3,8,14,16))
plot(multiple.RD, type="sidebyside", select=c(3,8,14,16))
plot(multiple.RD, type="sidebyside", select=c(3,8,14,16),
     ylim=c(0,6), xlim=c(-0.2,0.4))
# }