metafor (version 1.9-2)

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: lll{ trial numeric trial number author character study author(s) year numeric publication year ni numeric number of patients outcome character patient outcome (PD = probing depth; AL = attachment level) yi numeric observed mean difference in outcome (surgical versus non-surgical) v1i numeric variance or covariance between observed effects v2i numeric variance or covariance between observed effects }

source

Berkey, C. S., Antczak-Bouckoms, A., Hoaglin, D. C., Mosteller, F., & Pihlstrom, B. L. (1995). Multiple-outcomes meta-analysis of treatments for periodontal disease. Journal of Dental Research, 74, 1030--1039. Berkey, C. S., Hoaglin, D. C., Antczak-Bouckoms, A., Mosteller, F., & Colditz, G. A. (1998). Meta-analysis of multiple outcomes by regression with random effects. Statistics in Medicine, 17, 2537--2550.

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 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 (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.

Examples

### load data
dat <- get(data(dat.berkey1998))

### for bdiag() function
require(Matrix)

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

### fit multiple outcomes (meta-regression) model (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)