metafor (version 2.0-0)

dat.berkey1998: Studies on Treatments for Periodontal Disease

Description

Results from 5 trials comparing surgical and non-surgical treatments for medium-severity periodontal disease one year after treatment.

Usage

dat.berkey1998

Format

The data frame contains the following columns:

trial	numeric	trial number
author	character	study author(s)
year	numeric	publication year
ni	numeric	number of patients
outcome	character	outcome (PD = probing depth; AL = attachment level)
yi	numeric	observed mean difference in outcome (surgical versus non-surgical)
vi	numeric	corresponding sampling variance
v1i	numeric	variances and covariances of the observed effects

Details

The dataset includes the results from 5 trials that compared surgical and non-surgical methods for the treatment of medium-severity periodontal disease. Reported outcomes include the change in probing depth (PD) and attachment level (AL) one year after the treatment. The effect size measure used for this meta-analysis was the (raw) mean difference, calculated in such a way that positive values indicate that surgery was more effective than non-surgical treatment in decreasing the probing depth and increasing the attachment level (so, the results from the various trials indicate that surgery is preferable for reducing the probing depth, while non-surgical treatment is preferable for increasing the attachment level). Since each trial provides effect size estimates for both outcomes, the estimates are correlated. A multivariate model can be used to meta-analyze the two outcomes simultaneously.

The v1i and v2i values are the variances and covariances of the observed effects. In particular, for each study, variables v1i and v2i form a \(2 \times 2\) variance-covariance matrix of the observed effects, with the diagonal elements corresponding to the sampling variances of the mean differences (the first for probing depth, the second for attachment level) and the off-diagonal value corresponding to the covariance of the two mean differences. Below, the full (block diagonal) variance-covariance for all studies is constructed from these two variables.

Examples

# NOT RUN {
### load data
dat <- get(data(dat.berkey1998))

### construct list of the variance-covariance matrices of the observed outcomes for the studies
V <- lapply(split(dat[,c("v1i", "v2i")], dat$trial), as.matrix)

### construct block diagonal matrix
V <- bldiag(V)

### fit multiple outcomes (meta-regression) model (with REML estimation)
res <- rma.mv(yi, V, mods = ~ outcome - 1, random = ~ outcome | trial, struct="UN", data=dat)
print(res, digits=3)

### test/estimate difference between the two outcomes
anova(res, L=c(1,-1))

### fit model including publication year as moderator for both outcomes (with ML estimation)
res <- rma.mv(yi, V, mods = ~ outcome + outcome:I(year - 1983) - 1,
              random = ~ outcome | trial, struct="UN", data=dat, method="ML")
print(res, digits=3)
# }