metadat (version 1.4-0)

dat.dagostino1998: Studies on the Effectiveness of Antihistamines in Reducing Symptoms of the Common Cold

Description

Results from 9 studies on the effectiveness of antihistamines in reducing the severity of runny nose and sneezing in the common cold.

Usage

dat.dagostino1998

Format

The data frame contains the following columns:

study	numeric	study id
cold	character	natural or induced cold study
scale.rn	character	scale for measuring runny nose severity
scale.sn	character	scale for measuring sneezing severity
drug	character	type of antihistamine studied
tnt	numeric	total sample size of the treatment group
tnc	numeric	total sample size of the control (placebo) group
outcome	character	outcome variable (see ‘Details’)
mt	numeric	mean in the treatment group
sdt	numeric	SD in the treatment group
mc	numeric	mean in the control group
sdc	numeric	SD in the control group
xt	numeric	number of patients reaching the therapy goal in the treatment group
xc	numeric	number of patients reaching the therapy goal in the control (placebo) group
nt	numeric	sample size of the treatment group for measuring the outcome
nc	numeric	sample size of the control group for measuring the outcome

Author

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

Concepts

medicine, standardized mean differences, odds ratios, multivariate models

Details

The studies for this meta-analysis were assembled to examine the effectiveness of antihistamines in reducing the severity of runny nose and sneezing in the common cold. Effectiveness was measured after one and two days of treatment in terms of 4 different outcome variables:

1. rnic1 and rnic2 (continuous): incremental change (improvement) in runny nose severity at day 1 and day 2,

2. rngoal1 and rngoal2 (dichotomous): reaching the goal of therapy (of at least a 50% reduction in runny nose severity) at day 1 and day 2,

3. snic1 and snic2 (continuous): incremental change (improvement) in sneezing severity at day 1 and day 2, and

4. rngoal1 and rngoal2 (dichotomous): reaching the goal of therapy (of at least a 50% reduction in sneezing severity) at day 1 and day 2.

For the continuous outcomes, standardized mean differences can be computed to quantify the difference between the treatment and control groups. For the dichotomous outcomes, one can compute (log) odds ratios to quantify the difference between the treatment and control groups.

Examples

### copy data into 'dat' and examine data
dat <- dat.dagostino1998
head(dat, 16)

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

### compute standardized mean differences and corresponding sampling variances
dat <- escalc(measure="SMD", m1i=mt, m2i=mc, sd1i=sdt, sd2i=sdc, n1i=nt, n2i=nc, data=dat,
              add.measure=TRUE)

### compute log odds ratios and corresponding sampling variances
dat <- escalc(measure="OR",  ai=xt, ci=xc, n1i=nt, n2i=nc, data=dat,
              replace=FALSE, add.measure=TRUE, add=1/2, to="all")

### inspect data for the first study
head(dat, 8)

### fit a random-effects model for incremental change in runny nose severity at day 1
res <- rma(yi, vi, data=dat, subset=outcome=="rnic1")
res

### fit a random-effects model for reaching the goal of therapy for runny nose severity at day 1
res <- rma(yi, vi, data=dat, subset=outcome=="rngoal1")
res
predict(res, transf=exp)

### construct approximate V matrix assuming a correlation of 0.7 for sampling errors within studies
dat$esid <- ave(dat$study, dat$study, FUN=seq)
V <- vcalc(vi, cluster=study, obs=esid, rho=0.7, data=dat)

### fit a model for incremental change in runny nose severity at day 1 and at day 2, allowing for
### correlated sampling errors (no random effects added, since there does not appear to be any
### noteworthy heterogeneity in these data)
res <- rma.mv(yi, V, mods = ~ 0 + outcome, data=dat, subset=outcome %in% c("rnic1","rnic2"))
res

### test if there is a difference in effects at day 1 and day 2
anova(res, X=c(1,-1))
}