metafor (version 2.4-0)

dat.linde2005: Studies on the Effectiveness of St. John's Wort for Treating Depression

Description

Results from 26 studies on the effectiveness of Hypericum perforatum extracts (St. John's wort) for treating depression.

Usage

dat.linde2005

Format

The data frame contains the following columns:

id	numeric	study number
study	character	study author(s)
year	numeric	publication year
country	character	study location
ni	numeric	total sample size
major	numeric	sample restricted to patients who met criteria for major depression
baseline	numeric	HRSD baseline score
version	numeric	HRSD version (17 or 21 items)
duration	numeric	study duration (in weeks)
prep	character	Hypericum extract preparation
dosage	numeric	dosage (in mg)
response	numeric	definition of response (1 = HRSD score reduction of at least 50% or HRSDscore after therapy <10; 2 = HRSDreduction of at least 50%; 3 = based on HRSD scale but exact definition not reported; 4 = global patient assessment of efficacy; 5 = at least 'much improved' on the Clinical Global Impression sub-scale global improvement)
ai	numeric	number of responses in treatment group
n1i	numeric	number of patients in treatment group
ci	numeric	number of responses in placebo group
n2i	numeric	number of patients in placebo group

Details

The dataset includes the results from 26 double-blind placebo-controlled trials on the effectiveness of Hypericum perforatum extracts (St. John's wort) for treating depression (note that 2 studies did not provide sufficient response information).

Data were extracted from Table 1 and Figure 3. For study duration, the assessment week (instead of the total study duration) was coded for Philipp et al. (1999) and Montgomery et al. (2000). For dosage, the midpoint was coded when a range of values was given.

References

Viechtbauer, W. (2007). Accounting for heterogeneity via random-effects models and moderator analyses in meta-analysis. Zeitschrift f<U+00FC>r Psychologie / Journal of Psychology, 215, 104--121.

Examples

# NOT RUN {
### copy data into 'dat'
dat <- dat.linde2005

### remove studies with no response information and study with no responses in either group
dat <- dat[-c(5,6,26),]

### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=ai, ci=ci, n1i=n1i, n2i=n2i, data=dat)
dat

### meta-analysis of the log risk ratios using a random-effects model
res <- rma(yi, vi, data=dat, method="DL")
res

### mixed-effects meta-regression model with stratification variable
res <- rma(yi, vi, mods = ~ factor(group) - 1, data=dat, method="DL")
res

### predicted average risk ratio for each level of the stratification variable
predict(res, newmods=diag(4), transf=exp, digits=2)
# }