metadat (version 1.4-0)

dat.kearon1998: Studies on the Accuracy of Venous Ultrasonography for the Diagnosis of Deep Venous Thrombosis

Description

Results from diagnostic accuracy studies examining the accuracy of venous ultrasonography for the diagnosis of deep venous thrombosis.

Usage

dat.kearon1998

Format

The data frame contains the following columns:

id	numeric	study id
author	character	study author(s)
year	numeric	publication year
patients	character	patient group (either symptomatic or asymptomatic patients)
tp	numeric	number of true positives
np	numeric	number of positive patients (cases)
tn	numeric	number of true negatives
nn	numeric	number of negative patients (non-cases)

Author

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

Concepts

medicine, odds ratios, diagnostic accuracy studies, multivariate models, publication bias

Details

The studies included in the dataset examined the accuracy of venous ultrasonography for the diagnossis of a first deep venous thrombosis in symptomatic and asymptomatic patients. Cases and non-cases were determined based on contrast venography. Venous ultrasonography was then used to make a diagnosis, leading to a given number of true positives and negatives.

A subset of this dataset (using only the studies with asymptomatic patients) was used by Deeks et al. (2005) to illustrate methods for detecting publication bias (or small-study effects) in meta-analyses of diagnostic accuracy studies.

References

Deeks, J. J., Macaskill, P., & Irwig, L. (2005). The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. Journal of Clinical Epidemiology, 58(9), 882--893. https://doi.org/10.1016/j.jclinepi.2005.01.016

Examples

### copy data into 'dat' and examine data
dat <- dat.kearon1998
head(dat)

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

### calculate diagnostic log odds ratios and corresponding sampling variances
dat <- escalc(measure="OR", ai=tp, n1i=np, ci=nn-tn, n2i=nn, data=dat, add=1/2, to="all")
head(dat)

### fit random-effects model for the symptomatic patients
res <- rma(yi, vi, data=dat, subset=patients=="symptomatic")
res

### fit random-effects model for the asymptomatic patients
res <- rma(yi, vi, data=dat, subset=patients=="asymptomatic")
res

### estimated average diagnostic odds ratio (with 95% CI)
predict(res, transf=exp, digits=2)

### regression test for funnel plot asymmetry using SE as predictor
reg <- regtest(res, model="lm")
reg

### corresponding funnel plot
funnel(res, atransf=exp, xlim=c(0,7), at=log(c(1,10,100,1000)), ylim=c(0,1.5), steps=4)
ys <- seq(0, 2, length=100)
lines(coef(reg$fit)[1] + coef(reg$fit)[2]*ys, ys, lwd=2, lty=3)

### regression test for funnel plot asymmetry using total sample size as predictor
reg <- regtest(res, model="lm", predictor="ni")
reg

### corresponding funnel plot
funnel(res, yaxis="ni", atransf=exp, xlim=c(0,7), at=log(c(1,10,100,1000)), ylim=c(0,300), steps=4)
ys <- seq(0, 300, length=100)
lines(coef(reg$fit)[1] + coef(reg$fit)[2]*ys, ys, lwd=2, lty=3)

### regression test for funnel plot asymmetry using 1/sqrt(ESS) as predictor (Deeks et al., 2005)
dat$invessi <- 1/(4*dat$np) + 1/(4*dat$nn)
tmp <- rma(yi, invessi, data=dat, subset=patients=="asymptomatic")
reg <- regtest(tmp, model="lm")
reg

### corresponding funnel plot
funnel(tmp, atransf=exp, xlim=c(0,7), at=log(c(1,10,100,1000)), ylim=c(0,0.15), steps=4,
       refline=coef(res), level=0, ylab="1/root(ess)")
ys <- seq(0, 0.2, length=100)
lines(coef(reg$fit)[1] + coef(reg$fit)[2]*ys, ys, lwd=2, lty=3)

### convert data to long format
dat <- to.long(measure="OR", ai=tp, n1i=np, ci=tn, n2i=nn,
               data=dat.kearon1998, subset=patients=="asymptomatic", append=FALSE)
dat$group <- factor(dat$group, levels=c(1,2), labels=c("sensitivity", "specificity"))
dat

### calculate logit-transformed sensitivities
dat <- escalc(measure="PLO", xi=out1, mi=out2, data=dat, add=1/2, to="all",
              include=group=="sensitivity")
dat

### calculate logit-transformed specificities
dat <- escalc(measure="PLO", xi=out1, mi=out2, data=dat, add=1/2, to="all",
              include=group=="specificity")
dat

### bivariate random-effects model for logit sensitivity and specificity
res <- rma.mv(yi, vi, mods = ~ 0 + group, random = ~ group | study, struct="UN", data=dat)
res

### estimated average sensitivity and specificity based on the model
predict(res, newmods = rbind(c(1,0),c(0,1)), transf=transf.ilogit, tau2.levels=c(1,2), digits=2)

### estimated average diagnostic odds ratio based on the model
predict(res, newmods = c(1,1), transf=exp, digits=2)
}