plot.gmeta: Plot For Model-Based and 2x2 Table-Based Meta-Analysis Result

In gyang274/gmeta: Meta-Analysis via a Unified Framework of Confidence Distribution

Description

Produce a forest-like plot for object of gmeta class, the result of model-based or 2x2-table-based meta-analysis using gmeta function.

Usage

## S3 method for class 'gmeta'
plot(gmo, studies=NULL, 
	plot.option=c('confidence-density',
		'confidence-curve', 'cv',
		'confidence-distribution', 'cdf'), 
	type='l', xlab='x', ylab='density', xlim=NULL, ylim=NULL, ...)

Arguments

gmo	an object of gmeta holding results of model-based or 2x2-table-based meta-analysis from gmeta function.
studies	a vector specifying the index of individual studies shown on the plot beside the combined one.
plot.option	an option for plot. The default is 'confidence-density' for CD density. Other choices are 'confidence-curve' or 'cv' for confidence curve, and 'confidence-distribution' or 'cdf' for CD.
type	as the type argument in plot with the default of 'l'.
xlab	as the xlab argument in plot with the default of 'x'.
ylab	as the ylab argument in plot with the default of 'confidence density' when plot.option='confidence-density'. Other choices are 'confidence curve' when plot.option is 'confidence-curve' or 'cv', and 'confidence distribution' when plot.option is 'confidence-distribution' or 'cdf'.
xlim	as the xlim argument in plot.
ylim	as the ylim argument in plot, scaled if studies is specified.
...	other arguments that can be specified in plot.

Details

This function produces a forest-like plot for model-based or 2x2-table-based meta-analysis from the result of gmeta function call. The argument plot.option provides a choice of displaying confidence densities, curves, or distributions.

Value

A figure of combined and inidividual confidence densities, curves, or distributions.

Note

Revised on 2014/12/10.

Author(s)

Guang Yang <[e-mail]>, Jerry Q. Cheng <[e-mail]> and Minge Xie <[e-mail]>

References

Xie, M. and Singh, K. (2013) Confidence distribution, the frequentist distribution estimator of a parameter (with discussions). International Statistical Review, 81 3-39.

Xie, M., Singh, K., and Strawderman, W. E. (2011). Confidencedence distributions and a unifying framework for meta-analysis. Journal of the American Statistical Association, 106 320-333.

Yang, G., Liu, D., Wang, J. and Xie, M. (2016). Meta-analysis framework for exact inferences with application to the analysis of rare events. Biometrics, 72 1378-1386.

See Also

gmeta

Examples

#### gmeta: generalized meta-analysis approach ####

data(ulcer)
ulcer.o <- as.matrix(ulcer)

# impute 0.5
ulcer <- ifelse(ulcer.o == 0, 0.5, ulcer.o)
# summary statistics 
ulcer.theta <- log( (ulcer[,1]*ulcer[,4]) / (ulcer[,2]*ulcer[,3]) ) 
ulcer.sigma <- sqrt(1/ulcer[,1] + 1/ulcer[,2] + 1/ulcer[,3] + 1/ulcer[,4]) 
ulcer.pivots = data.frame(mns=ulcer.theta, sds=ulcer.sigma)

# fixed-effect model
gmo.mdlfx <- gmeta(ulcer.pivots, method='fixed-mle', gmo.xgrid=seq(from=-10,to=10,by=0.01))
summary(gmo.mdlfx)

# random-effects model, method of moments
gmo.mdlrm <- gmeta(ulcer.pivots, method='random-tau2', weight=rep(1,41), tau2=2, 
	gmo.xgrid=seq(from=-10,to=10,by=0.01)) 
summary(gmo.mdlrm)

# Forest plot of CDs
plot(gmo.mdlrm, studies=c(4,8,15,16,23,41)) # default: confidence-distribution-density
plot(gmo.mdlrm, studies=c(4,8,15,16,23,41), plot.option='cv') # using confidence-curve

# 2x2 table-based meta-analysis 
ulcer.2x2 <- cbind(ulcer[,1], ulcer[,1]+ulcer[,2], ulcer[,3], ulcer[,3]+ulcer[,4])
# Mantel-Haenszel odd-ratio
gmo.2x2MH <- gmeta(ulcer.2x2, gmi.type='2x2', method='MH', gmo.xgrid=seq(-5,5,by=0.001))
summary(gmo.2x2MH)
plot(gmo.2x2MH, studies=c(4,8,15,16,23,41))

# Peto's log-odds-ratio
gmo.2x2Pt <- gmeta(ulcer.2x2, gmi.type='2x2', method='Peto', gmo.xgrid=seq(-5,5,by=0.001))
summary(gmo.2x2Pt)
plot(gmo.2x2Pt, studies=c(4,8,15,16,23,41))

# Exact meta-analysis on log-odds-ratio based on Liu et al (2012) 
ulcer.exact <- cbind(ulcer.o[,1], ulcer.o[,1]+ulcer.o[,2], ulcer.o[,3], ulcer.o[,3]+ulcer.o[,4])
#gmo.exact1 <- gmeta(ulcer.exact, gmi.type='2x2', method='exact1',
#	gmo.xgrid=seq(-5,5,by=0.001), report.error=TRUE) 
#summary(gmo.exact1)
#plot(gmo.exact1, studies=c(4,8,15,16,23,41))

# Exact meta-analysis on risk difference based on Tian et al (2009)
#gmo.exact2 <- gmeta(ulcer.exact, gmi.type='2x2', method='exact2', 
#	gmo.xgrid=seq(-1,1,by=0.001), report.error=TRUE) 
#summary(gmo.exact2)
#plot(gmo.exact2, studies=c(4,8,15,16,23,41), plot.option='cv')

gyang274/gmeta documentation built on May 28, 2019, 8:54 p.m.