metadat (version 1.4-0)

dat.lopez2019: Studies on the Effectiveness of CBT for Depression

Description

Results from 76 studies examining the effectiveness of cognitive behavioral therapy (CBT) for depression in adults.

Usage

dat.lopez2019

Format

The data frame contains the following columns:

study	character	(first) author and year of study
treatment	character	treatment provided (see ‘Details’)
scale	character	scale used to measure depression symptoms
n	numeric	group size
diff	numeric	standardized mean change
se	numeric	corresponding standard error
group	numeric	type of therapy (0 = individual, 1 = group therapy)
tailored	numeric	whether the intervention was tailored to each patient (0 = no, 1 = yes)
sessions	numeric	number of sessions
length	numeric	average session length (in minutes)
intensity	numeric	product of sessions and length
multi	numeric	intervention included multimedia elements (0 = no, 1 = yes)
cog	numeric	intervention included cognitive techniques (0 = no, 1 = yes)
ba	numeric	intervention included behavioral activation (0 = no, 1 = yes)
psed	numeric	intervention included psychoeducation (0 = no, 1 = yes)
home	numeric	intervention included homework (0 = no, 1 = yes)
prob	numeric	intervention included problem solving (0 = no, 1 = yes)
soc	numeric	intervention included social skills training (0 = no, 1 = yes)
relax	numeric	intervention included relaxation (0 = no, 1 = yes)
goal	numeric	intervention included goal setting (0 = no, 1 = yes)
final	numeric	intervention included a final session (0 = no, 1 = yes)
mind	numeric	intervention included mindfulness (0 = no, 1 = yes)
act	numeric	intervention included acceptance and commitment therapy (0 = no, 1 = yes)

Author

Wolfgang Viechtbauer, , https://www.metafor-project.org

Concepts

psychiatry, standardized mean changes, network meta-analysis

Details

The dataset includes the results from 76 studies examining the effectiveness of cognitive behavioral therapy (CBT) for treating depression in adults. Studies included two or more of the following treatments/conditions:

1. treatment as usual (TAU),

2. no treatment,

3. wait list,

4. psychological or attention placebo,

5. face-to-face CBT,

6. multimedia CBT,

7. hybrid CBT (i.e., multimedia CBT with one or more face-to-face sessions).

Multimedia CBT was defined as CBT delivered via self-help books, audio/video recordings, telephone, computer programs, apps, e-mail, or text messages.

Variable diff is the standardized mean change within each group, with negative values indicating a decrease in depression symptoms.

References

López-López, J. A., Davies, S. R., Caldwell, D. M., Churchill, R., Peters, T. J., Tallon, D., Dawson, S., Wu, Q., Li, J., Taylor, A., Lewis, G., Kessler, D. S., Wiles, N., & Welton, N. J. (2019). The process and delivery of CBT for depression in adults: A systematic review and network meta-analysis. Psychological Medicine, 49(12), 1937--1947. https://doi.org/10.1017/S003329171900120X

Examples

### copy data into 'dat' and examine data
dat <- dat.lopez2019
dat[1:10,1:6]

if (FALSE) {
### load metafor package
library(metafor)

### create network graph ('igraph' package must be installed)
library(igraph, warn.conflicts=FALSE)
pairs <- data.frame(do.call(rbind,
   sapply(split(dat$treatment, dat$study), function(x) t(combn(x,2)))), stringsAsFactors=FALSE)
pairs$X1 <- factor(pairs$X1, levels=sort(unique(dat$treatment)))
pairs$X2 <- factor(pairs$X2, levels=sort(unique(dat$treatment)))
tab <- table(pairs[,1], pairs[,2])
tab # adjacency matrix
g <- graph_from_adjacency_matrix(tab, mode = "plus", weighted=TRUE, diag=FALSE)
plot(g, edge.curved=FALSE, edge.width=E(g)$weight/2,
     layout=layout_in_circle(g, order=c("Wait list", "No treatment", "TAU", "Multimedia CBT",
                                        "Hybrid CBT", "F2F CBT", "Placebo")),
     vertex.size=45, vertex.color="lightgray", vertex.label.color="black", vertex.label.font=2)

### restructure data into wide format
dat <- to.wide(dat, study="study", grp="treatment", ref="TAU",
               grpvars=c("diff","se","n"), postfix=c("1","2"))

### compute contrasts between treatment pairs and corresponding sampling variances
dat$yi <- with(dat, diff1 - diff2)
dat$vi <- with(dat, se1^2 + se2^2)

### calculate the variance-covariance matrix for multitreatment studies
calc.v <- function(x) {
   v <- matrix(x$se2[1]^2, nrow=nrow(x), ncol=nrow(x))
   diag(v) <- x$vi
   v
}
V <- bldiag(lapply(split(dat, dat$study), calc.v))

### add contrast matrix to the dataset
dat <- contrmat(dat, grp1="treatment1", grp2="treatment2")

### 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
### the treatment left out (TAU) becomes the reference level for the treatment comparisons
res <- rma.mv(yi, V, data=dat,
         mods = ~ 0 + No.treatment + Wait.list + Placebo + F2F.CBT + Hybrid.CBT + Multimedia.CBT,
         random = ~ comp | study, rho=1/2)
res

### forest plot of the contrast estimates (treatments versus TAU)
forest(coef(res), diag(vcov(res)), slab=sub(".", " ", names(coef(res)), fixed=TRUE),
       xlim=c(-5,5), alim=c(-3,3), psize=1, header="Treatment",
       xlab="Difference in Standardized Mean Change (compared to TAU)")

### fit random inconsistency effects model (might have to switch optimizer to get convergence)
res <- rma.mv(yi, V, data=dat,
         mods = ~ 0 + No.treatment + Wait.list + Placebo + F2F.CBT + Hybrid.CBT + Multimedia.CBT,
         random = list(~ comp | study, ~ comp | design), rho=1/2, phi=1/2,
         control=list(optimizer="BFGS"))
res
}