Description Usage Arguments Details Value Note Author(s) References See Also Examples
Produce a forest-like plot for object of gmeta
class, the result of model-based or 2x2-table-based meta-analysis using gmeta
function.
1 2 3 4 5 6 |
gmo |
an object of |
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 |
type |
as the |
xlab |
as the |
ylab |
as the |
xlim |
as the |
ylim |
as the |
... |
other arguments that can be specified in |
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.
A figure of combined and inidividual confidence densities, curves, or distributions.
Revised on 2014/12/10.
Guang Yang <gyang.rutgers@gmail.com>, Jerry Q. Cheng <jcheng18@nyit.edu> and Minge Xie <mxie@stat.rutgers.edu>
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | #### 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')
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