metafor (version 3.0-2)

dat.pritz1997: Studies on the Effectiveness of Hyperdynamic Therapy for Treating Cerebral Vasospasm

Description

Results from 14 studies on the effectiveness of hyperdynamic therapy for treating cerebral vasospasm.

Usage

dat.pritz1997

Format

The data frame contains the following columns:

study	numeric	study number
authors	character	study authors
xi	numeric	number of patients that improved with hyperdynamic therapy

Details

As described in Zhou et al. (1999), "hyperdynamic therapy refers to induced hypertension and hypervolaemia (volume expansion) to treat ischaemic symptoms due to vasospasm, and the success of this therapy is defined as clinical improvement in terms of neurologic deficits." For each study that was included in the meta-analysis, the dataset includes information on the number of patients that improved under this form of therapy and the total number of patients that were treated. The goal of the meta-analysis is to estimate the true (average) success rate of hyperdynamic therapy.

References

Pritz, M. B. (1997). Treatment of cerebral vasospasm due to aneurysmal subarachnoid hemorrhage: Past, present, and future of hyperdynamic therapy. Neurosurgery Quarterly, 7(4), 273--285.

Examples

# NOT RUN {
### copy data into 'dat' and examine data
dat <- dat.pritz1997
dat

### computation of "weighted average" in Zhou et al. (1999), Table IV
dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat, add=0)
theta.hat    <- sum(dat$ni * dat$yi) / sum(dat$ni)
se.theta.hat <- sqrt(sum(dat$ni^2 * dat$vi) / sum(dat$ni)^2)
ci.lb        <- theta.hat - 1.96 * se.theta.hat
ci.ub        <- theta.hat + 1.96 * se.theta.hat
round(c(estimate = theta.hat, se = se.theta.hat, ci.lb = ci.lb, ci.ub = ci.ub), 4)

### this is identical to a FE model with sample size weights
rma(yi, vi, weights=ni, method="FE", data=dat)

### random-effects model with raw proportions
dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat)
res <- rma(yi, vi, data=dat)
predict(res)

### random-effects model with logit transformed proportions
dat <- escalc(measure="PLO", xi=xi, ni=ni, data=dat)
res <- rma(yi, vi, data=dat)
predict(res, transf=transf.ilogit)

### mixed-effects logistic regression model
res <- rma.glmm(measure="PLO", xi=xi, ni=ni, data=dat)
predict(res, transf=transf.ilogit)
# }