# Studies on the Accuracy of Venous Ultrasonography for the Diagnosis of Deep Venous Thrombosis Results from diagnostic accuracy studies examining the accuracy of venous ultrasonography for the diagnosis of deep venous thrombosis. data ```r 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)| ## 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. ## Source Kearon, C., Julian, J. A., Math, M., Newman, T. E., & Ginsberg, J. S. (1998). Noninvasive diagnosis of deep venous thrombosis. **Annals of Internal Medicine**, 128 (8), 663--677. `https://doi.org/10.7326/0003-4819-128-8-199804150-00011` ## 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` ## Author(s) Wolfgang Viechtbauer, wvb@metafor-project.org , [https://www.metafor-project.org](https://www.metafor-project.org) ## Examples ```r ### copy data into 'dat' and examine data dat <- dat.kearon1998 head(dat) ## Not run: ### 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) ## End(Not run) ``` ## Concepts medicine, odds ratios, diagnostic accuracy studies, multivariate models, publication bias

dat.kearon1998 dataset