metafor (version 1.9-8)

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
doage (in mg)	response
numeric	definition of response (1 = HRSD score reduction of at least 50% or HRSDscore after therapy <10; 2="HRSDreduction" 3="based" 4="global" 5="at" of="" at="" least="" 50%;="" on="" hrsd="" scale="" but="" exact="" definition="" not="" reported;="" patient="" assessment="" efficacy;="" 'much="" improved'="" the="" clinical="" global="" impression="" sub-scale="" improvement)="" <="" td="">
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

Source

Linde, K., Berner, M., Egger, M., & Mulrow, C. (2005). St John's wort for depression: Meta-analysis of randomised controlled trials. British Journal of Psychiatry, 186, 99--107.

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 für Psychologie / Journal of Psychology, 215, 104--121.

Examples

### load data
dat <- get(data(dat.linde2005))

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

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

### meta-analysis of the log relative risks 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 relative risk for each level of the stratification variable
predict(res, newmods=diag(4), transf=exp, digits=2)