metafor (version 2.1-0)

dat.hasselblad1998: Studies on the Effectiveness of Counseling for Smoking Cessation

Description

Results from 24 studies on the effectiveness of various counseling types for smoking cessation.

Usage

dat.hasselblad1998

Format

The data frame contains the following columns:

id	numeric	id number for each treatment arm
study	numeric	study id number
authors	character	study author(s)
year	numeric	publication year
trt	character	intervention group
xi	numeric	number of individuals abstinent

Details

The dataset includes the results from 24 studies on the effectiveness of various counseling types for smoking cessation (i.e., self-help, individual counseling, group counseling, and no contact). The dataset indicates the total number of individuals within each study arm and the number that were abstinent from 6 to 12 months. The majority of the studies compared two interventions types against each other, while 2 studies compared three types against each other simultaneously.

The data can be used for a ‘network meta-analysis’ (also called ‘mixed treatment comparison meta-analysis’). The code below shows how such an analysis can be conducted using an arm-based and a contrast-based model (see Salanti et al., 2008, for more details).

References

Gleser, L. J., & Olkin, I. (2009). Stochastically dependent effect sizes. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (2nd ed., pp. 357--376). New York: Russell Sage Foundation.

Salanti, G., Higgins, J. P. T., Ades, A. E., & Ioannidis, J. P. A. (2008). Evaluation of networks of randomized trials. Statistical Methods in Medical Research, 17, 279--301.

Examples

# NOT RUN {
### copy data into 'dat'
dat <- dat.hasselblad1998

### calculate log odds for each study arm
dat <- escalc(measure="PLO", xi=xi, ni=ni, add=1/2, to="all", data=dat)
dat

### create network graph ('plyr' and 'igraph' packages must be installed)
# }
# NOT RUN {
require(plyr)
require(igraph)
pairs <- do.call(rbind, sapply(split(dat$trt, dat$study), function(x) t(combn(x,2))))
pairs <- ddply(data.frame(pairs), .(X1, X2), count)
g <- graph.edgelist(as.matrix(pairs[,1:2]), directed=FALSE)
plot(g, edge.curved=FALSE, edge.width=pairs$freq, vertex.label.dist=.7,
vertex.label=c("Individual\nCounseling", "Group\nCounseling", "No Contact", "Self-Help"))
# }
# NOT RUN {
### convert trt variable to factor with desired ordering of levels
dat$trt <- factor(dat$trt, levels=c("no_contact", "self_help", "ind_counseling", "grp_counseling"))

### add a space before each level (this makes the output a bit more legible)
levels(dat$trt) <- paste0(" ", levels(dat$trt))

### network meta-analysis using an arm-based model with fixed study effects
### by setting rho=1/2, tau^2 reflects the amount of heterogeneity for all treatment comparisons
res <- rma.mv(yi, vi, mods = ~ factor(study) + trt - 1,
              random = ~ trt | study, rho=1/2, data=dat, btt=25:27)
res

### all pairwise odds ratios of interventions versus no contact
predict(res, newmods=cbind(matrix(0, nrow=3, ncol=24), diag(3)),
        intercept=FALSE, transf=exp, digits=2)

### all pairwise odds ratios comparing interventions (ic vs sh, gc vs sh, and gc vs ic)
predict(res, newmods=cbind(matrix(0, nrow=3, ncol=24), rbind(c(-1,1,0), c(-1,0,1), c(0,-1,1))),
        intercept=FALSE, transf=exp, digits=2)

### forest plot of ORs of interventions versus no contact
dev.new(width=7, height=4)
par(mar=c(5,4,1,2))
forest(c(0,res$beta[25:27]), sei=c(0,res$se[25:27]), psize=1, xlim=c(-3,4), digits=c(2,1), efac=2,
       slab=c("No Contact", "Self-Help", "Individual Counseling", "Group Counseling"),
       atransf=exp, at=log(c(.5, 1, 2, 4, 8)), xlab="Odds Ratio for Intervention vs. No Contact")
text(-3, 6, "Intervention", pos=4)
text( 4, 6, "Odds Ratio [95% CI]",  pos=2)

### restructure dataset to a contrast-based format
dat.c <- lapply(split(dat.hasselblad1998, dat.hasselblad1998$study),
                function(x) cbind(x[rep(1,nrow(x)-1),], x[-1,5:7]))
dat.c <- do.call(rbind, dat.c)
dat.c <- dat.c[,c(1:5,8,6:7,9:10)]
names(dat.c)[5:10] <- c("trt1", "trt2", "ai", "n1i", "ci", "n2i")
rownames(dat.c) <- 1:nrow(dat.c)
dat.c$id <- 1:nrow(dat.c)
dat.c

### calculate log odds ratios for each treatment comparison
dat.c <- escalc(measure="OR", ai=ai, n1i=n1i, ci=ci, n2i=n2i, add=1/2, to="all", data=dat.c)
dat.c

### calculate the variance-covariance matrix of the log odds ratios for multitreatment studies
### see Gleser & Olkin (2009), equation (19.11), for the covariance equation
calc.v <- function(x) {
   v <- matrix(1/(x$ai[1]+1/2) + 1/(x$n1i[1] - x$ai[1] + 1/2), nrow=nrow(x), ncol=nrow(x))
   diag(v) <- x$vi
   v
}
V <- bldiag(lapply(split(dat.c, dat.c$study), calc.v))

### convert trt1 and trt2 variables to factors with desired ordering of levels
lvls <- c("no_contact", "self_help", "ind_counseling", "grp_counseling")
dat.c$trt1 <- factor(dat.c$trt1, levels=lvls)
dat.c$trt2 <- factor(dat.c$trt2, levels=lvls)

### create variables to indicate the contrast examined
dat.c <- cbind(dat.c, model.matrix(~ dat.c$trt1 - 1) - model.matrix(~ dat.c$trt2 - 1))
names(dat.c)[(ncol(dat.c)-3):ncol(dat.c)] <- lvls

### network meta-analysis using a contrast-based random-effects model
### by setting rho=1/2, tau^2 reflects the amount of heterogeneity for all treatment comparisons
res <- rma.mv(yi, V, mods = ~ self_help + ind_counseling + grp_counseling - 1,
              random = ~ factor(id) | study, rho=1/2, data=dat.c)
res
# }